{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## 卷积神经网络（CNN）入门\n",
    "\n",
    "1. 为什么学习卷积神经网络？\n",
    "\n",
    "计算机视觉（Computer Vision）是近年来深度学习推动下发展最快的领域之一。\n",
    "它的应用随处可见：\n",
    "\n",
    "* **自动驾驶**：识别周围的车辆和行人，从而避免碰撞。\n",
    "* **人脸识别**：如今解锁手机甚至开门，都可以仅靠“刷脸”完成。\n",
    "* **推荐与筛选**：手机上的美食、酒店、风景照片展示，背后都有深度学习帮你筛选“最吸引人”的图片。\n",
    "* **艺术创作**：神经网络还能创造新的艺术风格图像。\n",
    "\n",
    "学习计算机视觉的两个主要理由：\n",
    "\n",
    "1. **开启全新应用**：许多几年前还不可能的应用，如今已成为现实。掌握这些工具，你也能创造新的产品。\n",
    "2. **启发跨领域创新**：即使不直接从事计算机视觉，CV 领域的创新网络结构和算法也能给语音识别、自然语言处理等其他方向带来灵感。\n",
    "\n",
    "---\n",
    "\n",
    "2. 计算机视觉中的典型任务\n",
    "\n",
    "2.1 **图像分类（Image Classification）**\n",
    "   输入一张图片（如 64×64），判断它是猫还是狗。\n",
    "\n",
    "2.2 **目标检测（Object Detection）**\n",
    "   不仅识别图像中有什么对象，还要定位它们。例如自动驾驶中，不只是知道有“车”，还要知道“车在哪里”，并用边框框出多个目标。\n",
    "\n",
    "2.3 **风格迁移（Neural Style Transfer）**\n",
    "   把一幅图像的“内容”与另一幅图像的“风格”结合。例如用毕加索的画风重新绘制一张照片。\n",
    "\n",
    "这些任务展示了 CNN 不仅能识别，还能创造和重构。\n",
    "\n",
    "---\n",
    "\n",
    "3. 为什么要用卷积？\n",
    "\n",
    "3.1 图像数据规模问题\n",
    "\n",
    "* 小图像（64×64，RGB 三通道）：输入特征数 = 64×64×3 = 12,288。\n",
    "* 大图像（1000×1000，RGB）：输入特征数 = 1000×1000×3 = 3,000,000。\n",
    "\n",
    "如果直接用全连接网络：\n",
    "\n",
    "* 假设第一层有 1000 个隐藏单元\n",
    "* 权重矩阵大小 = 1000 × 3,000,000 = 30 亿参数！\n",
    "\n",
    "问题：\n",
    "\n",
    "* 数据量不足，极易过拟合\n",
    "* 计算和存储需求极高，不可行\n",
    "\n",
    "3.2 卷积操作的优势\n",
    "\n",
    "卷积运算（Convolution）是 CNN 的核心构件。它通过 **局部连接** 和 **参数共享** 大幅减少参数数量，使得大图像也能高效处理。\n",
    "\n",
    "---\n",
    "\n",
    "4. 小结\n",
    "\n",
    "* 卷积神经网络解决了传统全连接网络在图像任务上的参数爆炸问题。\n",
    "* 它能支撑图像分类、目标检测、风格迁移等多种任务。\n",
    "* 学习 CNN 不仅能帮助你进入计算机视觉领域，还能启发其他 AI 研究方向。\n"
   ],
   "id": "6bab013bace89935"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 卷积操作",
   "id": "b8844bd34f4329d6"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "CNN最重要的是构建块是卷积层：直接连接图片输入的卷积层不会连接到图像中的每个像素，而只是只与某个框定范围内的像素相连接；同样，第二卷积层的每个神经元也仅连接第一层中某个框定范围内的神经元；这种允许神经网络关注第一个隐藏层的低阶特征，然后在下一个隐藏层中将它们组装成高阶特征，以此类推。\n",
    "\n",
    "CNN在图像识别方便效果好的原因之一是现实中的图像也有层次结构。\n",
    "\n",
    "![矩形范围内连接的CNN层](./images/CNN/p1.png)\n",
    "\n",
    "注意：之前研究的所有多层神经网络都具有由一长串神经元组成的层，必须将输入图像展平为一维，然后再将其输入神经网络。在CNN中，每一层都以二维形式表示，这使得将神经元与其相应的输入进行匹配变得更加容易。\n",
    "\n",
    "![图片的层次结构1](./images/CNN/p2.png)"
   ],
   "id": "2f8d925afa4efa7c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 边缘检测",
   "id": "d9b89e9a06e80d25"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "![卷积操作例子](./images/CNN/p3.png)",
   "id": "ba114f9f38675b5f"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.signal import convolve2d, correlate2d  # correlate2d 互相关\n",
    "import tensorflow as tf\n",
    "\n",
    "K_vertical = np.array([[ 1,  0, -1],\n",
    "                       [ 1,  0, -1],\n",
    "                       [ 1,  0, -1]], dtype=np.float32)\n",
    "\n",
    "some_data = np.array([[3,0,1,2,7,4],\n",
    "                      [1,5,8,9,3,1],\n",
    "                      [2,7,2,5,1,3],\n",
    "                      [0,1,3,1,7,8],\n",
    "                      [4,2,1,6,2,8],\n",
    "                      [2,4,5,2,3,9]])\n",
    "\n",
    "print(convolve2d(some_data, K_vertical, mode=\"valid\"))   # 严格数学的卷积\n",
    "print(correlate2d(some_data, K_vertical, mode=\"valid\"))  # 深度学习的卷积其实是 交叉相关（correlate2d）\n",
    "\n",
    "# 深度学习框架的卷积\n",
    "some_data_tensor = tf.constant(some_data.reshape(1,6,6,1), dtype=tf.float32)\n",
    "K_vertical_tensor = tf.constant(K_vertical.reshape(3,3,1,1), dtype=tf.float32)\n",
    "\n",
    "# tf.nn.conv2d: tensorflow的卷积操作\n",
    "\n",
    "y = tf.nn.conv2d(some_data_tensor, K_vertical_tensor, strides=1, padding='VALID')\n",
    "y.numpy().squeeze()"
   ],
   "id": "70a6c545bdc1767e"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "<img alt=\"垂直边缘检测\" height=\"500\" src=\"./images/CNN/p4.png\" width=\"500\"/>",
   "id": "b1b76b43b591e6d7"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-17T07:58:36.444678Z",
     "start_time": "2025-09-17T07:58:33.872504Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.datasets import load_sample_images\n",
    "image = load_sample_images()[\"images\"][1]"
   ],
   "id": "1fd295848027ff9",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-17T07:58:36.897443Z",
     "start_time": "2025-09-17T07:58:36.452246Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ],
   "id": "da80cdf468a6a714",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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i5lgOwA2+HbFl+DnGg6k8d6ZYhjLbybslV54K32UQii5WwaAFY6RUaxfFKxSQEw+p+CzE/8VYZ94MAYQJ3ihjRmOUaYBZ7CtREciXY9K6D5YRYdkjj3jy+EwL5ASPuKRD511fVYbeogHB8jSIjVBS73gNOOftDrOjeOnkJEfSv/TOUDalW23oT2MqgXPsyBSZcYKzoZVL7ow2LRhTUIRYAU0bIM3AAtYi9TkiKWDMT5VWslgD0DGa8k1RyrvXW9UMYCVDHJo4hX+NcOusPg4EbO3Mx9FohQVwGD0YIsnD4JZhvfAUVjcidyFMEmhxUbYHM4SpIBMQHAhcOkUQK38A3G6O6w5cPbA3awoiCwvjKuOhxWW+yRyZ1+bkMApGlozKWCB0vrx/V34uhvGXBhv5IXMyA4UfSJQI7Q6I1s+2EDsXWdWvQIb2o5gOsuTj+dHZ4DAABw6i2B0bYagitdiReXkoGJaq22kCFhVS6u4M84VCnRSgY60sZhwyKxCQTC/AQs+h8BEFyR1ylIrKi8VgmpN78ZyfvLsY8hlVdxI4CjjkDQMRG+33XNpYyhOQrVu6nrSt+7TyqfWEv+bzcVkEsnJ4Rh+cuWHLNA3BQLBm4MEMB+s99pFeScrlwjbDzUjAsrkc/0S0uqaUtvUQxWCGVmyaeHmq/B2sZ8G8VoV989aD30S3jP01XRYY3hyKEhsrtIMnSjYt6PXFBnzD4gaLG6N6WYx8hGNtXmM3rNgIDxx+g9sDHQ3HgYOsl9rYwnHIszNHVS95FiQe2xFX73CiB2IH9k4gEjuBwWbhLQ6rlV2iSxh8A+uysHBFR6myliA8UuwIypOVGDo3lLct46tdSglyGK6v9Z0Fa2JFyYUq5RdCect5jfjGkNG4uIHf6Cgnl8+95U3heNU05Zp5r7acRwMr9lWsyOgWkq6L4fIIB3ayXe6gOYZeYwSqoiUCC2lTckwyxvq7HTBbw4koxhcoSsOfoM6KnqXuY0SxSAiLs76sCIz43zf1/L3u4FotGu5Fvty7RTeUopO+PyOqBgqd2uqIwL/hyEDrx1knoIKs3DpoMByWWyFsLRxrEjfDjabiF817MYdH8LDMGgUVKrKaYBiL37Bw28CGIZaxHiCrZB0pr9dtuLpje9YGPO7AYyDzerOQpBDvRHma+VCmiu2YMaQz6FNXeUUMXnu/ZQRBDAmdXnpONLBDFvopVw6fCw+cQkWeRVNVGBh312ooVSxkpG5uI3SCiVBRipA+VzprBeaM22YqFZR/Bj2SkpzMe5VSsifj+qZUxMko6e4jIpM2xmmEzvULemW6rhVBAQn9o3EZRspAjwuMYSclbFQ+PxmrFMF5zK+f1zq/rdSASa44Fm7/NEuwdjPgGgpz533SQ+9CvrzdCIvbpLGK4qyGO+XStDMhnmSn+/6KNNQtpVT7b62FPERFU9rb4jXlVQ66BRWxlH4w5A+tvKMjeLrWsRC4cHuthWOFd7osFpZ5OWQLB5z3CAKUDuNmMe6KqKhD3LyiAsa0Inao7pUi13yYaiWNpMMz8scUU2wveTYEYtW+mgTVB5kRI60n+QwZxxjFvSMSWIaCP7zmZMThHaHkew2yeyEFPITrGi9Qv8Bylxl1lYCuUqEtvlHPAOTBs84h6mGZMhHwCQnjmGfo98mPnkAAVmA1ty/y7+KtQEevxEezOOM1RlOG9aRzpUMEIAafR4z6nDFGA2YkrpQmMPL8InyDkXpN3TILcU9GSPpa9OR37lXoN7zeIk0gXRVYi4bdrPbDyhgozK//raKXGDVzexfeQ0ZpFQFzr/hG0xSwRmgwbADbVu7BjYWII3cTBNJrCsfNgccb8MoDN3fcArhFpg7ypdwPB0G0M9W2wWrBNfoZgnpSQDK3TvXQx2LQIJWs8oFihhOD698ejRbXuMCG9KT0vzLu1r8vqGLZi5805VpU5Fi6Wh/sk2CiVK1Vb+8BtK20vGVNkXySBUBzHvRwvIkiffQEtABoFN+vXofpnVinJ5hblRKlOyUsig7T0rOa45j3f0J/UUK8MJjF7HyH187F+v2i+fialAXXb0b2jyPpcGiNrdNVqlQ2O/IKj5KpAHfylNFtJW6F4IZiHLqo7IHWsiYicD/DynfaZkzvzMJ93alHRSnXqRCZhtL+ab1nyocmGIjIgkHj9r4VGQk4IjJ8H8FCP8mAIT28g4bMgLmNkjytMH9E7kjIrYPp/WPnv1keEaxpNIQv9hEYkY8ydCSVbAV5VXSxyHtJV6ZDq4ir3h+gTkJsgMprBLCeLsQdP1MGTw6NPPn77xUgbB6qhV1RNRq6XGmDDPRItgJVBQ4UcKooEvnBY5Sw0pb3CAkjIlD5cM+1zOgAb1Q1QtRf3lHlQicaaAz9UrZ28LUMKXV1j6dtwMlzGCnMEx2Hjj1fO/7WuPbQlQDGxFBpimGD7gwMqjo9htw8cahe/3qLNIEm1n9r69npWQIISgxYhnysmjBQGhgB2B64XE72GKoodaGcpehPPmjDcLMc/o7A9gxfbzcWAwF7LzzuwKsNXKGeAwqfUSFG5pQQGEpx0BViwaHIZQSAYYyGwAOnkFlr9W8ydmK4sizNatYevJoKyeyrgqB2dIS8hlZiBSLvdERo0SQbsy8B53RfgGpc0U5KWG3znTTJscmL4xYhk88RQ2Gt15LkdWa5952jBLr2X0P3dohdwhdsjXA+UDUOlRcsAwSmHvJ3tymEaGW4DsQdHXttx7jj7vNpDGQNYlw/PGqJ9UL2IbFlSoF3mkURaEZyXJE0hYZLY9PIMQIxoxvGyZ/J/xq+jlT4gTulKWKVgup5aE2e3DeGDrl7cudkaTzVWEjGlFG7DGcz/M8fC+7r3wQFsRF+K3mJzdSLa2aZElowhG3EzuiADS/MIvsNnBwGbrML6Q+Xvl2iGH/uXASz0gMB1dK0h590CT6K6bbVi6OtiQ4qdw6rUnOWTBEEOXLEusCQnCXd0HFudDMdXWNQmPkUISsP3aouadXcgLOTQp0mvQUG86X7eb8JBjM6PGgWYJFwRiIzBZJjWvDsBWFNK/Mhz1WAiUFglM3J95SOBQFZlLEH5KygxbXqkVD0KRYX20yW13Pj7rP63p2uoP7OYeT4ymJoHQWCTA8dKefTNkimJ6ej+/8PMNALSFeLzSKA/kxXrDGADHNzUmxEtCPzaGtRoe2NtS7ZiMOsFxWp+HNemR7YyALBwIJ7YDN2vX3jSsa9OnB14DEMNwpfcSbDjxE99tC8agVa0SlbXlqNDHom0N0vHddtxXq2mOiQUgssCnnG6d5zbUFhT0ZVwYgh92Fo7NMI1bDngkKhWTs9COfv1UszHqF2YyEh9UhVcYMFcdoug2TvFQP8oAserR+BkkAbayYpl6EO7WCXIY3M6y5uy+S9Z/cvw8HcZXtEU0ToiDUdprGenny9NwY9gFyh93mfk2c2XqfeDZFzjF266zAgLOsBdniasYMyNP4Ha6MPbuubVdRSyupoIwPS9QRkjhNoJe/aE0ZoRVseq4zA615j2+trtSNGWkr5zhuA7iMQAgThgN0wIwNZ3X8D/IoECrc0lL5r/RMQBxar/I35Z5luw6ri3RMvBgoEuGcRYEWbNboRxclIhgGxB+itUtomnQcj3iwoLMMlPrPypO1gBGuAWouFm3tWzgPcS8/qe8+URG1R006PU+QRuEf7Wkbt8c/3lPZTKiV1sDEcHZ6pjwOtJytoQQ9EDsxJX4401GATzAFmCiSLymtbbdAOLeriw7HiQPiG87oESIFTEarmqzSNXqeswFCQXF9o3DEMbives44cQKH5nXQ4sf29LtGj+w1T5NSQC6rw+ow8iGcE4OqDYSh8POANXm+VJgC0GG00qurfrHRmGgLlasZ1nLSHYzFuGwB8R99Tng6rNZcBt21wW3AWvGS1sOEqT597ULMFcRr/xwi8CuAqAlb+EYM1ZXhIs2J+GSKvUFjxqdmZtgMESXmhwEWcF5D6HgpfalVZR1DFL6Ug6iGAKdYyaCplE8ro53tZUOt9bSANxvSOa39C4LApnBnVidhPAKUafeUtgwrCampd+KgXi8209lV4knuLRZamoP7tO1hTlXPkcy1YQKcIkhEwHy2Aho4qlCvGEshQIRSKLqlMuHJUpKbq/FIcQznVemthtVZHK4th97pPh/7DNYthTEvmrYDIigMbC74Mt9pxk8Ava216XEm9hcBxVoTi9dB9VcI8FnYydo1xaEjx5UjDVC2Gca1maJJKiVfjda9gbj5Ez9GMKL/PLXxsuZwtbzNVYJ6RgDb6fJ8hUt/5/iUCfku6eBhwZFFcguaDMxttXyPD9nAvzzDlKNfduL1MSt+MW++MfLnuUpBlaARAkuaS2NoutrRWaUTyt96Db6T9kgy7UnpVMcMlIp1W14zAPbu5IsEKhixl0V/Sr1Wdlc5GkHVK0pEpDUULQuPuKyIZu2Wv3pMdG7x2SidmMTkQBG5RLGeITndpjiZN7izEFAjAMKKcLgLwo3j9pKOrmDkp2VHf4WlrjK6J3BvnQasTu7ddwJx3PUO6YtpVr9/zUUf7HHVbK5tW92WDKoFg3H3lt73eooAwUVqy3cEqVoX/uRd3FIkZSpfVYsbOHtQyxxkBo3c0DFyG9zI8udeRIKC0pGEbOw6yw1c2EzG8cuDRDFcYHj13D1QIS/I4wEuu4aIiOs32bPPnYtXC3n8jzovN69uY3S0ajU0yxEwX7NLXc/Vl9BYFRykBzaX0jakolUotNQ+mfyLetohTBKdoNQxBRGAdCe6SP6kELUPY2iJqc16wUlwSOtMdmTYQjIgIOPfrRgBrvS5NAsqs4AuZ3Y42ZHNy7gSXGaFQEyhNa0BBWAR3Kw0AOwQ7yvCtOzkmIDgphGl8x5oPCliPpOhRfGUHAU4+PCMC+fctDB4XUR9gT40GDQIpC01tLfTq8d/nNVOc23C93maf1rZYubbADe/fhuE3GxRomoe8dWubma/s8kf3FrV1SwYfgdxBkOuv1sDmjsMDBqcBZ56f6QGdO1JbYqtIW7zb9NpcPytDnvRcwaJLGtGAV7RTctdh+KgK+WyGxDC/7ktVZgT/UL8MWuLYascNILx6tqgNdI4/ADhD7EyJUOG2M8Nn6P4qWozAyPExChEMw2tdQvlAlCpCsEj5qHH3eR1aaEVi6rYFZiDZLWQjnmCxYdF2sOdAEFUZFZZ1G9p9dbArZmxg0wHRPBGoFp2M2mSehVG0exDsvYYJIlbf56TJgN75ItEwkqDTrk0X6bMBDOp+InpdSsBBPcbGQ5hfKZByfsxsOvQkev07Xm/RdEiePT1UU+czULlb0ak8W1tw9PYwW4u7AWhkzKoxk3sgjjXitQ43HRKRaC7CpC4yl+SbTYWAHXlEyaMHHj1w20LhvUBtqMs0cXZTRfd8Ma6oxfumkO/8lpSqE+1jMMAoBmrwIAFB/ZtYQv3778wKmaDsuJq9gAJVDTkaICiSVDUHp7ROA45SaPqiiTJWxs5qgKioGSeeyqr4204IN+5+moJUZOSLAlYU1toqpYKnuyreFApWFEfAJCgl8L3+Eexgic3GRuo9nxGnUD6uw1yAlKuh14zvp0IAOpc4v0degJWXchJPKWzrVchbr6p7vDHa4irwmmmLafQJLEPAoJZVRY+Sz1VrdbrPbp6pzwGcgGqt++TXeAIiRKpssEMFX0aD7Wf3ziZBEahoAI1n/qSxw9bvgcCtr4EjD2PZeWCXAAN3FeRugLyv02Go1tZMl8COqpepragCUBjrDgIpl7kaIFBzFi20yvKGGRIvnhmeXISMcxGs9EeMc2Cy13+ChNxaJ07KH/VniQio3TvUc8EUdl4NOOa/9TIugRJ9aUBL0qgSzK0iEIAaoE/DYxWxy/bZ6v8AGulmPeXrg3KT/9hgQz1UkUXqQu2OWIBvFswdWjcmEHdGQhJ0JXCsbbEySHFw+6eiUME5ax0wUgkzFN/00t9R35+aLcQYJQ9PPv8dr5NolX5JfXcGAyKq9I8UNDAqjn7r683TBG5EqKzCRXosax21J3SGZ9wCYQsLl0pd5OElGdZenoLn1NkBwxHyCvPiYFQAEXDP0+c2CRQAbhG42YFHOB7Ds8FQZH61ahmA2m4b2jYEIbdowHi/LpOoxAEmZfu7XvOaKXRrMNL9bWz+krmEYKUzdNqgipa4AohszpEekhguaylgiiA4GTy71wFU0rjT+fqv+ImKJMx7CkxVnMKFvEkW51oBRpHQokPvncKKtifWUZqoGrhLIehYYwlU4xFt4E8tgiskG61UQg+VspMXq2Yk6CiRAKlpDWRoSa11t2iKgkUa4JOy1YKalafVnEAlKDoFhjzVUHEDcEMwvW/cmaQ14oWLEQFbNebcCSKjv2h4eh4zZF/R+fsirlH30O20qR1jsVobpdyiAAY6X+00eCNy0wV0NOoG/ivmnj95zkB+y7MugOH98FumCfyG8Bt8XwHuJMjzA8T/CQbWurAtvU6gzHEm28jwt+KGjFUcQKz2UIHy0mcKS4Y5xIuGSoFYcJkQiFEYedpazKhARdica7243dzAyJh0rZfnzoqc3Nob3vqhRmb1X8GIPiSsuJy/81ClYbTkta+gLIZ2W5QWqW+XTg0wMmNFT5DWWQjaT5QM9q2kuxnxuCEdzsjoThzsZouVdSDWp9fiSAOcZ1fwVmxTbA64ziewI3npRiLqFFOBtDmnU8R3rHjpFi9d0Pqm/12K3KqOQZ0wy4sa1wc6vQGkQ1lD0doD08C3LtcYv8EJ/h2vtzqbYJnSAlzwfBx/kolU8NqKJUP82YWLTLYWsmUlKiyXhiQNWU+FdQKWa3bzqueBYeEG4FUEXoXhxsJCl1CTE7o4iT3AI86OJaZavn+d3w0CgvJQx2Vmd195Ahq4KPfI4xSmx2CmxYUXM+Z7U6xBT8fsyL2+AWQBGsOJtIaF7yv+rSiC7tOPz3lmDjSHuqqoGsBQXz3uhTyCNf9oo1vKU2OWdbazGk0yzJRALXI/Zegd5fPS08P8gEocKRATEESnP2x6zyh7hsp/33PDfShRQguFN5MfZeKFslRQ1QJPPoRy7fn3Yvj38N3bzmzUgaw0+qEYboEV/hQYICghyEnPQSeHcrz0grJ2RGOKM8g1oA56SXRGwMSoSQ4KyoF3tEQ6cTf4KGxkdb9qJKRQ7iwIdE/w63o/UosjiwrBMwmMUQAnIIA7Duex3sGTBYMepcLvyiljpI/sgPox5FLpM8rLTP5WNG0x1Ua5qvRVtzVOL13bpyn7odooygbHUCojksYzghxiST45a3ITPJn4QZwXWdDHMhceXGVcMtVDibtbBnunDjgH6QbJVY5znWR4yNyTV9NsJAbQTorVc9ugGhT6bq2RtTedyuyoR4Li5EcLw3LPehDsio6pq2IQTRXtVU9RdLZyfjBGPiNEqhchksI5umwjDW3o8x3orAi0HeN3qV7xUfQcz7sDMC48LeCJ3LVazAHH/PANX28BBozDkwHi42kYFxdW4c0FG4goKm+VnmUqLC9jxYInKjZHF4Y5cu125BHDuZsgwz5XM7z0LBTcJs9ZixdNTComnVF9Am+T4Pobo2bA0AV/oW9TGO6NxAR5smz3KCHme/eSFHfXOheajF2Gyuv+VasrmWfKpkKFNAJlXDkpnZF+ULGp5fD9eusZp9f0xnHWl82WUUU/9W7R5yxAze+5bqsyC1FC1XdBNciJMsZ2Ag6af4mrGHM+ngbY+Xt37pNimtMh7bV2pzC8vMGxcryPCdTUOR36++y5AXmy3sUyt60oXPLhgq1FEHYfrbAxHv2uRe7PR7UIAEW3SFPZ9PvX2N52XuOhNGMSPZq4VcfRRZVlfPVTO0M2luVuEBUKJhhwZP1M5N884dLUedB3GnyGgFd4nlK4vWsKgucVhLjiLt0juZaXT4Kko6LICic4W8MSnHerX6U5lILLYrxKjRiqSM4NWFSSLhpSzpWKrSxVqLBvNS9TX5kKDBeQKVUaNBmWiWnLIULJRUWyxhICyCZKdzYxB8VzTEpmaQcIOKYmPAWlSetTi2bZhZAd6YvPWkgOCdePDkrJMEeVhzwp2hhdLDjGBAS7tg4tsQyhFtADcJ1BwKQD+vukiw0bYE8+Q5sXFxBsXXGabykOVGr4ZPkHYGsQFfVefu44rWaR+80AwZtvLVR7SNwhO1jnXg0MCY/tS7A6e90UXQim/cgoFpayvgK+23PPc8iyr4CPPhPZHtxwXQvXiDxlEG2caq+rFMFASikA0wTpu1w5MZuY6x4rzMXnAs7waulhXaVoAFAfGKw6cJ3vrr8N5Y7XvfMzhwyFpTK0BazIjQ9ID6+6BIr5IDprXPJIWIYUypke01af54g4M5W1OfPxXtqQqkzI36ZQBGoXrbxprc+Ut5AwsQ4gEHU09slISSBjfER3ShEpwoWaW5CRVPzqisLMzmHlBUoBD+06wt6THrNsQCs55aXVZIfgu+0tdfka2WALrTgQ5zPnczPOQrYiYiX8at6QZ25V+CaCaXAcYTkhg9GVzirQMHlwKCiMXw1Qh86ab8jTC6hoUC1o20XaqJ0EWQ2Uhl+A173+Nt84brl9EHHFZd8QsXN30m0jtwxk3wFzh0d7uRX6ZpFlBLJ42TDKeGhIkZ0IYlW5KtiI99S9UMa0NroagQhlRb0OM1pqBSKcvJ+O6qYxjaal514b4VNsFgBTfwaQVfYBhHNrtpHuywBnIepSjBCkryJEUcav+MSQhyWV5PI7IeO4ip/ye1Fg13gfJXmL80NORd9ngjGptjqABwIHbbCLDcVVDoByEWbs3x88qpp6VbpjAVgp36WHdoqyc5sowDbmVdPA+YtfAZyiICWopFCcKdZjVySha7n8ZudLjNfdKZOKpcTpTRG6QZWiULrmtL333rb87teb1wyMBVGXQUiNSWlPj9HkfUZ5RjxtHIZMJ+RWKMeB9PzDpQDzu85+BM4CHg9gL8eOlbsGArhJXUufyOLIqmAY4yce+Zl5z1YGZYROXr4B94SeRjKefopGGBjGff6uzydwAMMigzmo1NLaR72jIVUlQChnnpou1DBl3H8EyTmEo5S4ATxQpI2oKpUn8AkCjCIJ2vgtCXUQvesxHEKIbkMBOWdaYGA+W2Mt4dPaYngPNF0SaBU8WnqmqQzPRv00hhKsO2O37Hwu+unzaGUh8musIoyUFMA8tUE7BiIAt+R4D+t23AasxQ24Qhgla7rPoH39GOL8hnSIvpg3XFTQTlBYPQWGogEGo2iBxzbUqcPK48d4ZtSQu9XwkD1tf61IANeXcmdwxL4BiymCfcXhG8AtDX4kADj2xnL2G0DW0ORPGrHA4nY1dFsPWOqfO/2c0aY2SOI1B6CjvI1pTYw5pnm/IJisRBjWOrhbZTeQ1R7yJW5XLU80cCGdErBowE7dKD6zqs1YAHmeNF5pKNXRsOTYeCgZWo5NFAqteY9DxjffXSdgUG2bJ3tw3LPAuXhPz7Ms0i27oa+3GDZLId8kW6O1a7E6lN/vZyyCpmBhvacOlL1SS2AVWRkwdwZZ9RNo+tQSTPNRanCAZfG3NWW6y2SUHJtJHkZE73R/O/+ruY81KkYItCzf0Q+k39O2969/vdURxjx6iCh0odQ4DU2DBI6D3pkqU8OBvZJLnPneUyU1r9XvOlTmxgKZmwVuCEQsPCKbCrUJyWdnHRIV9PR2mqqDYP23jcXtARkmyAE03sxldQGb1L/V+vBiVO6//t5DkQ5GANCltlHMmdw47kFaHCYVBdl/Hv7UYTvIuEqp2OL+as7DvfiNNd9tjA24BN+fru6q3qdt72jKRcxSoyaPDGUUwNBiKYPT+uDMQzV2/rKkeDsc1vl3VSOTU+kBd3Wy1Jsh88QjZFxGcoGNsDlX5eKlxFqBNQtZKa0ZFgpYVVWf5iglQMObOso7bVMgzubsMszK1quzzwfGmEBA3SF6nhMSixXVgcXdmBX5MJaEGhXkWNu7mUKCkrsEcnwC/vUd04jbT+RNGyQJ2UfG/iSq8BZb1QcAmRbovgI3QEWDsYF9g+1b6ibfCDg2c7vhTUsbNBrCDlmElI8LupFaoFNDbEq0pGOsZpbimb87gBUHU26ka3gaIIsuEiTPhEeBk5P6UT1G9UXhR4tpwyC4HNEtk4Hjdr86BZP8HVjZrc9kZFF0WZHGWS2ETekJYFhClN7yqktq+xNTH/A/Rnr3mrYkpn7oKctz94iqcdDuiIoolE5JIy/V7PA8CVfsJQ7U4CrDE1DENdP+Adimjmd9zmhupG9ldC3qfdl8fth/lBE/EaJtQgEBXnOPfoYDpVt2ejH68+KVNWQWkF3KSBoBAu5TMd/8evM0QXlVmreabwCjIwtgPBWORccKtWnf7B6rYzQUN1+nLTgKqUXkAUPXZVBwzWJlFIEUqBOgtE1J4UcRvMY18ylx+mcaFL7R1yo3p29W+XW9Mz8tIennStKptL8hujApPWJpvFYFVigh3sG9EUtnavOYYz0DSO9IZFnoe4aK244yrFPtaGouxQGg8qG6wnJuenLP8Z7xdKql6EBDUfpI943xjfFtbwHMCHNA3dbylp2XXqC3bGxX7MHCd4McAlXhGw1ZyDCWZlp0/iV49nRuEbxPdHgR9/NuoHUmhyUIG+wm1tPyJm2SImq/PtuopzLnMxfK4Gm3DOacpBNksAQkKC+tdyb9BfjRkaAxWJ39cOLepV0j4z4zUsg7F3+rOBAaS967dhlEIKvOs3YAfiOgYc3AJjDYN7Yf1kmGnJcLGDkbKa6mnQyZAQp/V9OhMCx2dIzIVvHpwK1835E7Vks2G/SoBMHiAoGZAsQnGooD05hbrK7ZiRNVUYW4uRiolBKdkaWiD/MGiaZU65RTZ4g8DZ/JKM5toiE6rTt+QBZsVooApx0LFfWJvj6iIbt0kp3veOKNDuLmGqj+tK63TjkGAT+UJR1G1yJrG8rMhiF2697a1TP37UdwF4Lmfr8MbS+STWnvTMyxWx5rfoOC0fSdDoipBsN4jQgmeo46wjsFgK4fO4aC4FikmwaYeZPXW3QgvFMyZDLlMnugxkroFrqMBOhrCteKYCmI1YMazII66wW4gHkIVQruDYFbxKgFoXLU3rsy6nelm3M2UvBPQIP1KlqryVaMYow2YWf795rnlSdiqBUWAyxUWLTqBGDlFeRrvD8qVT0yjqKNlGLWWX07x1bbhapTnQSgwcdk5zDU+SIW6ZiroriKFHUtT8+D68AR7QthCBKtFMOklKNAwaSZlq2blZA3al1Lc+SYo/lMdzvMnkQZFD3I+5EORk9br0PRgPkj5SSvwBgpiLG28xUl8CT8eRzr6TfMWqkFUtmmgTj6XtpP7nz+msWBc56vKzREhZWzuLENWvW9N1Earfjq7lFpF+m2hWz+hfLgDIFd3sypyUpNLErZrUDt81fBYHUcdEfWBmQvAds3nkC4szp8byyCAfOMEODmUPOqSRWjVx5DLsDtlxlssdLlxUeGBASimxaOdRFd/G39PW23Y0M2sI4HOhFSERudM2I0CLgxUjeIbi1fyUKzDDQq6lTFZlPFAFlTsLqQFhGIFYjQDgevuc66NyVytf46injuOJDqP41wYBYNxEZonLse0Qw09D9pf8hYwhjvnfxmJQY6IwXIaHMcdlaxvmGL9U8eqGODGZkFmObhVr/sbGmo0JQM7El5FoHQxEEryrqU87o3AwEUyFIxsxBQjOgib3zqtzA6+p4jW7IVoxbpGGkOnenxGvv3utdbpQmAO1vJIzg796ZF5J7X+3BvSiYXPLgNMBk9Dm4L5EIfkZuJNh/o0mORxxNHTRiobQcRgz2H4S4DLmPYK6mQZjG2xlkIMt+s0OXpZfWZ0VjIiJ2ViLgGpTz6bcMMewNoC3xSx43WC0BitOJtSpdeNyl+I40hdG01P9NANDxRQijHUIVWcT/0QYcq2TCgDvawBcTmcxpY9ZDPucs07AJ5OnK1Q9ch+nIOJTvCSQHIX8nhd6oi/9/8kYec2AAjLGQ6IbuRStCa3tt+o7HhwkwF3jHBuTxKkXiBM+mXJzIbUtTWkYO6znpdToyrn9VjLWbgdSGyNQB48uy7uZ5qBKwCsTXPOH1xfKe/wnVIwK7UT8xdBOwhkPvLFQ3L2oAjHMfOqIDvK+CPCL9h6WyCK3cWDGV6wmKy2MOZaYM+36cBte6f0jn2zP2H9BbQ3QH5oAbIXANwWxtWnj7l0n+MDNT59jTe0vVzXfsPrdowwiNSoyUyJO2UaVRUNFuYIruARq2bdLAbwYuMFdIIGaNlSg+Y5UFPXUNwZpt5IFWPdfCFdQlxOdRmqEhK6B7SIyDPeh+OJwNKYFRqne8rQY1QlVo6VOtg1DB6vKtatWs6rWHHoCm7ReCROpDOk7GWwzcYEOB5FOKLaNK5wY6jHiORLABZ8qz76750fpWKVsTkZKwno//211ukCVCeVuhHHr4UahmBGFvX5IXwLkalbDxel1EDhwN2sGYuWFdsuEZ6SFchZwA3k/FPquUycAEHgi1fK8T4bTlkWGpykBFvZZmMNs3Y66jCz2LUUMzrtZjDuJ4Wx9DMUyHZhtEy5PJI8n5+GpOZVeoo/4473EKj+jqDM6dBCjnYmIsfVtDppPTb6BvHlMKyWnjJnNPY2XgOED2/HnwZnnF4WHk5zjVZE1XcrWPfB5mXPTULyhXa4dncTvuNNQ6hzqeDPuuGMr5DqHXBawAAzJg7jX5WMxoQqgNyxNK2L/lDs2gxUEB1ft8GsfRLxACkuoQ8XnUHzbVt4FtTdjvnqdtazk+825boNH3Nu59BLqNysyJZwGITCNwSGLLxkMBAHhiUBYN2ewT2DX7z3BIXqQ1STem+Ga1q4NMrFXBgHRmVsea5NIpp9OqsChrbrHVK4+7jXjLmmmsA9IbVqAu5jir2gtbGkAewUY7Js0pPlkcMyZjkALAIHGpRrDXgtSlXU+at+DVLsjdwcDt3f6tnNIxl3jrGGoNGdvWSFx1QvGLeziHVcUVoeqgtXy7jpdIpyZfU56DGQu6iAFBRO9likDYF0pH6IlOqEu02/iEHxFLbpbjmOsWYr+jQLfdbodazymm4V7azqZcmlDukdsVQgSdRAqUHyk6IK0ZcljUp2dAPw04oNXbgTV5vFRmI8ZcOEkKd3a1xpjL3FVjVe99IDFTPAUcre0eiVbMsgvMIHMg+BDcPXC1q18AS+vbikMkBaASVi9/5WWn480xKoQtaarg+cuTFrzJlPnLFKr1LxdnPGEZFxWyrvYK80bRiU4HyvrZRObQpabUMLSIq+221ThqAzM7nJvN7PUW3ncPC3bDE9AEw1KiPrJT81AQ5ZUlmfxfj9k96dwOnsHJdozWusfSizAh9KaUKB9oo5KqSpbquBcnKYFa3PBnfUlS6Zw20Hlp5Ux3cElyL+5ePmMHZ3tabGeKOBASimbe9zf2jaxAhWnninhjo8Q9FKUV4Bs2TgPzM581ew3fW3nfVMBQvdIqgvu3exom5/1DdgCaqPLtSAx6ZEggHbhuxc9vg4dl+2G8bvjf23vCdzoUhcBxdDxJ0MrLuVekLsUjA10hyWmCtYOfPJI4BjAYAQCpuH15r0iN1oel3VzosU1jiPbJBRXu6CFzcyLReHbdrLHqnDI1IESqCJvARDdxqGe+tdMAIjCJutC8pIxFGmzL5S05H5qTzM9M7kG++InlhC+qFdgAYdxbYKdPZxbjSViYynkSuZIC6+d4tq3379a6iCqyjIC1WBNws6x4qTctVEbClXZIe1fjAeZTOiBERAOZgh5oYqZuhP0bSpqhXgDJEAICFEGOikleO3a3THiP6ENhtb6bMvkYdve715jUDy7At0e8y6xMyIxV/hmTJmER+25NhlTHQdpRUTFnRFQCuyIUIT3ToYdlvLHhYC8NVCM1rFEg8sTIVG2uDc9dq1ej9adtJvplzEvvDoopR8tZ5bRrn1QJbRVNChH5eON4752zMYS02vGDzhLtIQDEcUBGWCvnAZeFhsNwbHPIVvOhLS8X9tAqQU7Sml8rpVjg60nlRVXXU+KWsL9VW02rAGJKRY8gQ3UHKbYRpn3xw7NPKK5SfTO9UJtpmV0Y9Zhe0u5doIvppPqZTHBcOenMB8ZFoqlcrChPIrbDeXaK/9CzDlJzzgENpzEqxi9BUlqFvKw1imf5abatTtkgpUyU4+WHtGnv1GbDBK1VdRUUUbTS6n4L4kfR30qCLNtJgj90LAUCtiGvroCJGPngslNuN4u2+g7cXs5D8pCLB2NzB73DfeQCRtgoqkRie0YCwYsucsWRDw28ZlSFKY5VGwcwYASt0gADy+YfM3cK2RmSs0+Ynq7sb1loMI8y1WshGahHRhdLbEetW96nXRfyK2iGQuyRUnCyA0gpfzzyG4yCQsbhbRPJZsb/jktfugJljrQvCu9ZnGuJAnjETJnCSMtHxqfZIFQYPBGw5G4gZrjSSDwA2DqozURKUH+kAUi9ArznvaHbAjWeLGKAUS423IpdKFESpRAvAbXP3ResbHQFvuDTwttaNXRvF31joZ8g6iApQi7Vp/0q3CRXERhX45gJWhCFBgIyknaJ5BQ7q/ugC59ODgTpxbGHorTtg8Fteb9Fn4KyEizyWQ+jp53HDPGgzG2LA+nNLdHCLyNDekUaimhDxnh5sQcytTBJWBMqD6ojAmHChTf3ZiljvJQDrApua053SCinWsSOgEWNgErmDAsLL94tA4okZJ6qzZqmIu68Fxjz5oXq3A4XCl9Yj8rN8ujwGkwajxyRDDKguWEBA4bNs3pJewh7Uc+/vqhfBnF/du6IRUYxbwMIMxbh6Jn+18Kae1ngOd77Mav++iuGKLQL1XGMjn+0OXC7cdcEwb4U/ZTjunlUg55te4iDWVy+UBxy6fzKt2KMAQS1wybUUvpZc0R6ti/U4eaN5LEGbBo1dwGE8KlY3/gmUoknD7RwDzsV2YP8DyccpsqAo1FQOlvxDA9AHIPTihHjYAmB7YvPA2rnfKDawbvndFYDtALZ6CwCnbnunkUpPdCpNHm30ReclNsrOahBJxkHqo5HcGPwgCS9vXvyucdCTdzOoE1idkHgsFq2Bz+jTNYPrYqRX5rsZPW1bCQHJbBOvGhvOj8Wmp/D1NLzlaYteetZmW1+Zv3P9RdP6zG2pV9ohlNDqlPlFo6ct5a2vCQNkeIctHBJSY891UhV+jj160mlLIhq0WYOEQJ6JI6dOrFNNjwKjGZxWUZFQrR9pJp4oQ4P7X5o3yxQwXF8FgXwuveWql3CMjsRTnq2ve2Jb4un1tby/TX/1661OLZSoOR8q5O2Y4aAkvsJhq75lZcu0z3YHuDg0OsydOdCHEom4orqPvIgmXGhyGCQZqkGIJlm0oqoP7XzfxTmbP7luiqPuXIpHHnBpnBjf01xtvBc9t5DiNPFb6xd9FsGCq3xPzVecCleFWjLEp9z0+F1pBBFmTRprLoja1CCjL4Y9XTqnicmold2s96bMuMdwZsc6KQw99lnH+MlrtD5z6Wd+tQFo2qGoZjRY8+CZpGvVOkyvjpZUyuoJUADARGjLYgE33qvAwPisTQs6BBjnW49rM5Jgdc8q0manwMIzBW412F6YWq4CEyJsjCjRYH712qv915J/gcU43acdlZyLHIBTVKAicZSpeuaGheOIwBEA3OHb8QxgLUAfWwyPPIV2Z9g8H9qd9bT/vOYdIOAI7riaZ58AdUpfRfqyLiC36vEMAlgZyMlwC9b9FtzRVzBtYEBY7nxaNHoGVK67C8mGYQkactJ5sTi76isNxQtGJbFAsGOB4HjM2JlSlDCcjCL8xud0VEKRnDzVMwsoZQgLU4yfXu1mrdSBKMfixvcOBxCGbfTyRX+0I1C0I41LjxBYeo0/H6Ziwory2So5rRqM4aUbqFtof11R32G0y4GBVZTRyEM2nt1q4qzfK2U2uwFW6oU8tjpxiX7cDB527ZKu0TxsagoRelw3IitP/v0dr7erGQhQGZEYRChuqsSW05rUriOIOa9lqn4OGl8dLJQ1xBvO8wfytPA6ZZBxr5PC0hqECLXOBlTh1TF+0xemkb43ahgFTWZpeNW7fmq+1qZ8thYKOFH/BB74wLP03I3BMINPQqalOEkLizb8ZVym8Yz2/ip3OwuKQrTTZANK2hXT83sCSjYUV9OKyj/YDnTMr6uck0Z2R/dDAlTjs7pnjHWWzk8fSeH4QbSJmElSj8Byx+W4ZAQjgGMtPLpjeTZgUQh/UQHEYBYp0Jz1WBox80nLzgQfTsDm3DAE/cVKL4mOWbS5xU96iLMpCr1CuPeBRWAYnwYxjCH+ZnbeXorJeixa9+JZjZGUXYw1kX/EWa+h/pmffQCjRkiZcosGsQIBeTJhcOvgxsUC2wUOvPsK+Oa55ef+BBbZOjjtqhS9PM2c02HrXFYRQO2bV7idOsxX6jaXMaLynWknA7C8e6dkJb4XeZe8zMA4gC2XxodHueqMDfSaGVMXpHsuBY3J0eN/on9gVdCpWGGeykk+0RxMRX9HgZNcnyy0NSRIzyXSfAvancHAVLBDx3XUtd/OYZDHxPoWBWjQoymenFuJkzSdmpAxbRPd300Vb2TzNM71fRP40PeTj9Yack7dH8PpmrY7XVY5dcX8p+/23/y9dt5p1aifbQCFef10GE/3G/Q+6aM5jiRw1To8sXGvf70FGNDOgcxDylgEWnhgjdK0vW0eDqVqbYEGzWwjQ7gbWZcQMkicRbBbWc1Vv0xBouAXA4p2hZTixJz39zmHMq3ytbkuqhtoA9XfH4p2MsaJUcZzTkaAytPGZcIBCtsVAOjP6nIKsYQrQsnTRvJ9SIly2329xpRFTsPIUcGlnrQCb1lrca49AAVO4AQ26FSzXAPYWuUmBeOlWEz0nAbNGhhIXZzqP4zzpgLUJCRjni5zy00BFt5DFfFFm3PkJvf169ZjoQjKxmI0TTDXbXNuoz7AxvMD0Hn1HSHQ5P0sZ5A3HDRm4ifWnDhoxFW3cTCN4kPpTAYCa1eopIWkAxAyCuXhqaFzPEMR8c0TTYs+xZhdxFTv09WNW6YGgi2F44aDRxHbTb3+teYq0hP4XKxFEU/mHBR5pEnM/zEFEKq6Ht5dgVSLUaCJllXuElgw1nQohdh8avqdcpTRLa+1NAOLb4ceKpUmPWP8m/ooDPA8rhdHOmEBwKPD/9UXADqPgGBAwN5Um3VuGidjZrZQHR7FBqNgLYCqi2h/dirA18JD8k7LtljmEImGulmMJOdK+gmrJu1W3284UC0zxa45tjtbOhW/dHqnaanT6vsEGOLZsh9TDyWNTrE8hSRAG1mDsqJlNkuTnor+jNHiqv0B0M4n16qePI0+0Ir+/DbGlSf78lteb1Uz0GEXtAJgJWE3icjtEqoYvRt6TocH7GT4jSAAhpuMuZSOcoqnIi+7o0kzvlpxDoQAgYT8VQs2R8UFjmB3LssQJKhkl466KbMFQEUiMiJjhmV1+G89U8/XeIwGfijnAQLqRz3bJxARUKDSyONcHQJHC+m1OMOtyWtSwEFBshK6idaFnDUVCYdHYEXmAMV/Mnh9phqqiKzaeQZYIMWxRU/hvAJ5v/vDkEQ3zatJMfOTTZr6agRwsG+lRRZAOYDLAw2BDcMklsnK875hKvSTgA8DiKBhKc2UNFYkZA7oXCMQPfHT+NkxkgfHm8YUyIiAxiJDHVS350f1c/QY6Z2FoTysliy77tqoOo47Yq6ucZ38qvuksD59vmg4+VzA3nPHAMJxCcch2d03PHCRM4QfGFaza0TWwoqud0kDzfoAiG/5JffMXRPURnbPwjoqrjXJmt9qNQdAYf7c2hrKe9swghNACKQFau++OhvnckadG5CRqWwd1g+PNuKxEcjiQPOuw9LyWeRZCIFsSXysS8uWGdypj3kqrMCI8bpARszMVlbdw1uNRt7DKtJyfhnlsGzmvSEyyXvz3AHULrLJSEY6eX2iOTJNEpvPGAUw0shmOPMkmAokiOZuEI887+a+cBHijQI70gdPrZfx/Tg5AkN2DjoTvQitO6qZ2QQJms6oUZto6SSLd0Se0UY9cz5bZHrD19ulCUwLa91sY1SJV0MOVxcyYC/gOEb4yQxxHNgArjLaplwQJ26GigQIXdVAJnFWKhSGNE8AQUtaSlrSPTwdtNGt4ksE4mLM2TTxyzu2WVsQvQCFNGdVv57LZ7NLWyXi9XkZfx8/MYSoAcEKp7PvsJ17sVnEAZ03AHV1i2yyEYjq9HbabhQ8/CQ2i6wOroPBFqqTYA5nYx8ZHUiv56Cwkj4eRVbxg/J17pH57YDqwVOhjqJRKc8I7ko2GkfRHkgvcKDzAgXD61sDsJrnVjF3Ab0Eb4s232u3CH+cgmsADmDhgKtQzZFKsfVCbxW19NR0ME5OlMV/iypueBgTTEBrA8AFRn0nCJXy2ORpz3HVOochDofhxucQANji1r2A2SUnGxzfArrTYXupa7Hy38cYEUAcuTQywodRGeq6EbYP0KsUaCMvmyOO4EFCUT8rHIff8LACdrvC4obDVhaR8ihiD3YfZVis6oECyDM4ErzZyoOJYoAdA+RcY5V+SqNpAdgOxJFzctJbtUSBxT38jltku/SD41C0QXWMOolVMCSAjCA4jT6rzs0zFaQtgnVyIJLX8ugNQ5hnOiEAiwNhhkdLML6oe4XybHSAzRNLV9cZw6CjuvOchk6nhHd9U/pbC48rgZe5lwyKlm2ojwLhaQ+TtxcLFt3VqLjhWB1pvbKNepx6U6Tey5blhoN0bVedTI8L7JLUWgBC2+u4WsZW0SQEv64toYPrFusKghEHOaActXSxQEapcJ2RMI17AKU7aD9sBdW1oR0L1t/EUcCzHDBRSsafOqxoMLcZznTB/asGqnJ+2Qx883fuXm8MBpwKwKyDRVpu2ZcdjrUOCgjBwxr5t0KRjhtmoYijioFsVJkLcq5pVAfUqaQ3zu8L85F4Z3A0COMsurPmvfy6QACBj9mJqWxuVaywzmvuP5GbBL2Kp0ZltgrQQmHFKAYtzxDDyOeC5Ng9WLzUhkXPrJ0DkV5WPo65TipTlKdvXR+BwOyFbTKyODOjqNs1IPnVXPuMJhS9ZghaJBu0E29pa2rNRRGp0/Keg5K5mzOoaVFRAeN82wjrccYaAuZzlUpR3vkY2yAHf8Vp8A1ScE8DrX3JDFWNBSv5B+8E+u8Cluh1T5cJ6UVLqLzfZ0+CkheMCFeQk6xp0HQk//Av1zZX3WvIYmislosrltUwO7IkBaRI1eD16jKI3BWwNy5wPF+GtfOzRQBg7iwe3cPwi/IMd1vAXfkbrosBuTGQhwNBRXwGy0MuUutEFCePkjRsVi4FDXzLf1LBTQBNRpbTtqZDBX2ChXSL23+RQKV9G6stvOKlmzEVILnk/xbBckbz+twExgChFI1FYC/tqc+amUCeqWBaDmQNgtIdQKYdFJa2SE96+R3H26aaOkcOizeK7SUfQwdrKyrQYXDdgNe0jrXxYb8TjJaE9OioMyjHo77auxjEN1k8aWOMBBIxHmXoccTQtTqDZ4y96YLxMtQhceUg5jrVxU8Muuxgv+fjM8Bq/Gei3Y2k+HVqRutL3uD1FmcT9LM1zwBGEVt+2DsAOuSqQh7VE9xiOiAqqEkEqJBX2swJOIaiRX5WxB5MgKGgW8lyoNPqRyvljApIuxlKwejSOFPzdQ1z+lVf6ufrOXeKuH6if9fWwAYJCu1H/Z5P8PR2JLCBMv4O1JH0i6DUDS00Cl4IdDHOXFXrhYylkAGUIGqOXTwIQhwzYMm4lqQY/28IbY9qSva/Q8NM+q6IgfgJPtDIvQhbili/p+HqHSUEGgILkJi1PS5lQ16rCnMVms320rq/ohaTA4bHghHqjskTs5hOd3C2Tl2yLkl3auYqJgwDcKFRupcJMJpTFXNxekbEWSG34uI11fZRc285gMSnIhrkR4G3muPu5+rwIIGPyO53Fws8hOOBwGHJeDq9ViYeK+gXaShzhdcw+KLtKtDq2MhdBRpXz7lNeAKJ5BkCBbOMPllydGoxZcR96JrVrB3pqfuRhw2tnd9bpva+Kw9Tomz3sdqARRb4hRkLR8lqrM+KENX6pEq3g2DAADsyZeCAErWwwAUpQx6eIMDygbZGLZH0BfL5FhmpqbEVv/SBWxklVNrAhz47WUSKZZnLoSX0VhbsuaJNw9CV+aeBud+6LEcmr2EsRtu1fTh2q+VdvKyCzV7IVfanZbanUxUSY3rn81Gsp14oT6mjOe47mSrd2LQh0wx5br3Vmgo9xvvxYIi5Dd0Wdrc63/x6YzDwQN1T++nHmGswlqJnmqz0XiGskL7Sf6DqXPqt0JaU/HCVgnuKbmT440zEYsQYzDqfGQ0O1vywYrB31/0Wopg9/VsSXPcBzoOfCnoUHQUaBMT4vAABvSYa/PSeqIwVjvX+3GF5DQ1kbc2a3BeyOY2C5TUt5sQE9BR6LGVGplfbV3447tOMutg7QEVCd4sySDf+5jM0nojz9QVsWmNmhETFgrAKi9c6BiqUnBXe0ZBMw1p8l8qyZPme509RjeZlATLT3wC6OFXjmOvMz8W32kcPoNy5koFA8mgA+wDs6BCcOX9Ik1mYyhqdymeWPCmaE00AGe76rorSeJ2KPyf16h5yP/WjMWeBYLC+JSMC2ddn7Q2P29huFohKPeT3FzLM3TJuAA/cMUZGsoBPwDiV/IwJChSqVgYImKWpP5BGZhvpOGZntrB4XdRaB0LV+KZdCJmWUeQnWGCW+8g3fwfU8S4xxVJJ5ACtadZtqZ9i1lMhkIfvqLHE6pMO7WLJF2R3+TUewPWy4e5Yi7t3IhD7lvUFBa64zrHUJwzZBXOuLXvGKP0t4znksuWXESh19kTreYDsaiOdNw2xVH99pi/VShJAoQrNpT7lwHfEzRiUMKj1swB/22rKiymdl5Oq9ZCtsLtxYugMKZhWIsPoz0mfL4f0g+FcuDqATtF1gKKT1dH40PTWYV0NmvBGrzcGA4e1aIk/qsgJWuyR61iqYkUVKXtE5sOmEiq0ZCySYrhq9ERfEqriFKALuzrEtNZi6FBe/0Ra46VY3olIYzGraHEadJyNf4EOa6JM0FILMSxR4QzORaF+rN7GUuapQ6u1LUvFkTtbuQa3ZSlfv0w9y5HXF6gYIS8ZdyHPQdeuZrby+jr6Qs/NTDXuUKtVGCMCANJjM5TZj1wrNSSp3Q85qjboetFbMaPhxjBU5dFjjL2X4O78zLYdekONWUoQh5GTF7seal076yleir4xaXMqYJweOIFS0XDSfwCYOe+SsLQaqPPKHbB1aeCxyTeqcYET2NKIrgM6LyLnR8ZTxKb4nDwegLlCuU3jap5Er128cu/7gIa++wgAAkB9jkT2eDh842CXwWNvHJHrttRpcDvD5+n9qsI8O80l+PFQS5mAEH2St+Xe1OnPompKasoFZggMCCCNY0zROBDmMLvhYNGXKx9vG6Eq/iBF2I8kDsemnKYIBrTDYXXlHGwtbOm4MNg6OvJEgKHjdgRigAOurpLMfcvQAsBBZ2IfKY+HAwsOtQbvfv0HNjaOSJC1RY1ih0CoqDEs0w2Ksrhrw3cVA1fX1snOJbJnG1FSMAB00YQKMk7XSwKVJhnPWLWgpWsMakPeugkEVQHDgUMSU9+TDjIY2cmqrouqLO9Xqt5wihLqX3nKNj+npLzOINeg7f7Nu/fOn52jXDaABvn6PqL3hq+3ShPYyrBUtVskylLzh9ovDtSCeDiWLdJG1bjI71YOPL9VCmhOvlZDnE8FMDwz4DW0m3Z8Go0BwNJe66KjlS/IIHZB2O01hJBxv3torfsIR8nAa7GqcVAbZW2bKhAAGiZDAiTlmQMA86qak0BVBj2D4b7O39dRpTWOHLv7fg2daadMeVUbu+6iANQU2Xw7eOvhPQVXzXRtKqHgnAw8f11jK6VhtdtE9CkjePeaBZH6vXL3tb5z3mMudwWixYEyeEuacRjzJ+utmpMJCtqgC8DamObUQk/SFOjvCERlaDYNSlHAuQLbs05Cp1StMUduSWvJagWnhdQWvOSTDZ2vXgtdiz0UtMbTRBt1EAIcGeZPfs3zRC4rG/csDzwgcEHkXv2d8rBorFirSTPfinZZH7vdFeaONipaOi8jYmFQ90CDMYCYf3XuPkoHiGuybbWMCVMWYd0nBWBdBhCuJslZ1Y+V8pNZBav1PpAs7Z6bAX0JuqZxj6WUQc5vsSGQ9G1kdRp8HRypqtb7KG27ROkAdeG7OGdFx0Pty7W5CPSuYxF8uNJaaTKXb+4GyhbSWSyZtUqiV/F3DDkEC5AH2KqysLuowCw2jriTC/JgGe0yrK2zDEqLdhcSZ+1TPjNpDmOnWx1vjDu9MoBL0tYog6AezIji2cje2SMChSdpbX42yir7Ofd65c626ZvzflNrASMKLtqsVW881Zyvf70xGFDnKA3DI/OX2dY1SnBzo2A2G3Jz7EUBjj6SVu01e0GVf+7ZNyMYaqO3mOEO8kQMZRRRxTdFhEJ+giONIE6MN47+S4G48X6yiP2qBhhlKLk8ZXk1br4pgy9lz2r6CkWrYEpAgAJX3jCLqhr5Lhr6FFILArBwtm0l4/HksHxGj6+EQcwDtLEQ99uTaedlO9dd4bQ4tPMAsLXYDdGpUA3a7sXAaTNsZPVx1R64lI3GOMLuHOKqZY4hf08BQacRkAVVvrOZyzpwBXDYpi13xHEw76hUEdfQmpdT5k0abxAvGigVr438skBbGfhitjQgQi1UZp0y0P0Y/dF5GBVVukB9Syv0iQx995HYB/L8elWNy7AIMJkYIFNNUmw1xeg5VGc/EByQ14o/aBgiEH5L2u3csXGDY+2NZ7FwGHBxxwpPMBDAhbUkvhdBwZHbwJKxEDTw8o7zyFke5sSoU6iGiGPRgTtGS1EAwZBefAQO8nCoOyEy1WIWrEU17ulPQ+k6iscMyx6wmU+XgYR5NtkmAA9bbBPMkL5nmD0sUxLHOgqoucL/ZghkXcCSoVgHg4ILth5O+qsRWerojAyK/wP7uFH2CVgJBA43rJ3g4bAEKXLSSp95VvmvFUzBMQLFCJCiwGKRU+RwIOAJhid4h5wHJB0Y8EDYorjlmD2s1pCrNLYnUhwoF0sdXknzBBGrxlggT/8tB6JlIgMBrK9B7jBZgdotp2J63eZk+OvcjpMQp1ytIS6nFxduqQ5orq/Aa/M2mIIiQXFS1Hqv9FSUs/67Xm8VGWgoIAIrJ9p7TWPpCNvgLoJRsiMPoxRi06JsKMZD0BdXYNJ64RCNMNMmt1Iuz7zoKSU4trXUP4NjTwMY78/PT56gLhUzTWoBBQCqsjpGQRXQkYL95JoK9SuFoNC9B3TeuxoJRagugIYHAVWfrzGiFOoxQm1/Eb1khIk2q6RHnnoNnSopgsMm02qBS0cxZM5LFO4DpMR5f2g9vunfvN+AcqkoEKxv4DPpWdX5FTKMqsRH1WeXMsrZiHdIa+3zVo3MvTwV+DO0leT365rx/hTW8Us3JxKPI9MAhvQYt6MaCWl7DgJYWR8RPIgqJ3JgnAqG6tA+Gs+UQl4COcHPZJZqtTQJCPTVPIwGZ6a2mCYQSFgOLHc8WG4FO7bjghsuETjocRoBQZPXCnhZ5N56t8DCJY8YDkdgFy9o7GZOsWgeNNO2Mh8yvGp9spmUolMqQASy13FHpRboRdoeMmFA3KD+Gua3BmDOLY7idQuErdwquNKzNG5H2zDUSadckzxeMaMEdTqiXfh+AoncKiqeJ1gptcG1WDrM56i5SHc4uM3SAnvpILNcS7fuq7LMgHVh1ChTDz6iS+tglEg6iJ/U1jhKullzU/XgMJllG7JVcRtIU6h1s9nqZUQa5vqW8T8BRgRGLn4UuSU9ershHzl4Q/oTNe7iKTNsKot63pSTJzbr/k/d+DWGOca/Y03zraF8OChp6Um3YehQkzda6DtT9U2vt9haaHWKnBg9c25EPSDqUjvMolVgy0gvGZ7XEMPu/uYdAaC2pamiV1sNPT0m05awu9wygDJsmLfUSkPP7bqDfk0TinEt3xvRgB733bWSUEUI5Hzx+6FiK3qoBQZY+Fc7C/iTtQObundXBXZGPfp3gbHTvLVwZBaZ4LKB0+ANvjpPL4W0wcTwCjh/BXzyPn2THov3s0Xl2pKmTQ6vYZATeMt7O2nkppGgCrSIfDgeKXoHgoVfDsSRhV5dSQ+oFiX0zABgG4gDuO3mHY3nRKO4G3vQlkaDle193xOtSVt5dVon3V+tePW3G7BueYNY6IgGYPJOClSI+l2ljt1ye16jVpxijAAQW0qVPCuLzOLQPMKXkaoIrGtgxcZzcxwe2DfHZQUuvrOglHvwV+EJrmCWgndra9OaAYEr5EE6LsjQdVe1L3lzp1A3AYC8bHr56fk5z3cwFjimtlBp6aG+AyTGIscvW4x+SP9pncl2pgPaMjKSf4sl+J0wVOvgKgokmAN/N7Zkt9xn3/KYn0FLoNoC7SBZ1C8WDTalfKiXdhzY4QBSjyzLNfFg3QaaJx2B42aoI4AD6ZgA3YxKimC8xPJa1v64DWhf0/VO+V2dfNjqoRyB06l+VncMY3qv6pcGKBBwWzqoa/C8GaYJ0XdPgkCZL90l+lfp6z2wOY9PXyvFalp3yqH4bEZOChycbnjSP33jO51Z47U7HfXNrzcHA2U1kAd5wNjZKhVBmA3+lLLr6t3M3QW6n4AIMyc5tZ88OgEALY48r8j3LM5M1slAvnP3jBM6ixpDKqSxUPM7J2IXt6JDMeOS896d8TPYTy1iBQAEEKhcUV4XQ7OuOgF1G+sq5jLG8g70dMucqdEY1TYbgrhUZJNSbXx6R0ALxJPtleicbhXsBypfqu8ZgsVV9zw8RK5IpCTSEAiAIdhapF61+V5EV5EThEVYNkkycDtUtj1VvUCMo2l77ZNrQxX6jCiUdxM4K6P67mt45Y41ziwU/Rl5psK4k0SlZDdUyq3jf6u+5rhUCiHFReBjofoTGOeshdCo5pnJJbdgDln8Rf6Vgb4vTkWOLyNZeezwQ6TxOHDDAxntUKpr87sb3CUQBXxTX2jLXxIgwC2D7MORUwnUdlcyUYbyObWqovE0pjaji71mRuMYxdHO7bp5WI97MKdvLCQU/VsC1LtjQwd7WdEzZZU7QMyIS5jCq5D20VEBWwAyEpBFo1bGxuyAjn6v6IXRATOxIqVjkY8YAUHVHQXgG6EeEnZFhGcEJHhc8lqITSeO+x3sCOWKc/1z8fLeVXTXpYTa2RMWRYcnKr4FiIKy2KOm+ycEMv9vSPoaDfqih1/rb0DWT0ip6SFGHpo65TyA03bBJ45If67j7Ce4qDdqEE/QwHhrAoOR9tb9lHYsnUQ9rUJn8RRByCkKOZ7QtuztXm/egZB7OevxZdOnuTW0ECKN2OjyZOwl0OZ3KKAq5gqURxnjmknI8rRscAOA3zb/af8BWDEJw+J2Nx69TudpSlPOf1lEph3/o+nPrKxOB5T5fkULSqnmv1XNrM5l8v4j0wIqsDIKzNIYVuZQkySGZQeN2TA4jCLMc9dTNwhNjyIetIEHhlE2Y371LtRGFBKgMcr4Whpl0x0HYCkvf6w5x3QPBM5RnkCzS9+zeKD4RVyaHuBB/SjPI70MzzoWRVC0RxnIPW92g7ZwZah1Gvn5vMmbg29LYcw0ypzPNKQEAVQG6b13YVVfk0q7iwxVR+DAvtKo8H2oVoP5xjI8kjH0OFS1x3XLt1eNUi6TKuwzlI6KaJnkgF7/JXIr8uHAQb5+CMB2sPjM6pmxCf64djOknABkFPWuBYsDZtlZUvvkYVYZtA5UMtfMo7JNUUVSPcW/jw0GecqKl2W4c/0WMiKQO6EkKz6iYtRslvPZlKUnkUlFCXSIkNHzPy7ktYWwB+bN+fk6oHoJC6Z3ckQZBYoBdGpuHSHEsdqIewB2IPZOuV8L8GseDmVdxOgWiL37zICqFwAkpYu6oMAcm0KIPsYK4sJHoghp3yQZzoblAXcysGXnDTCmT1rirO5bB+LZUXqlbbB2DWGAxQYAk9/aEdRoW+ecjbZeM49YRrHu3ZPEuA59j4Xmg6lU5ziG6jkLSOv407VzPm/xevOthQwPWS26QWGTVFg6oZCKSOi3FD8YDZFoDEVaxrxmDJvgAOjQTr0/iHWH9mazkV4n6/t7jFywnQlc8SxdqzFMJQ5kiHLaKirEAJ4UgUWHL1N7DM8qAmq6UhEET0HD3sS0XjyYdFRIVVM44FCnswVnWDT2CA1qX/bkEM5TB6A84R0DFS4KKLRRU9jOcxu7rdr5WKvDqIfARtV0BGj80F4xAtrx6Yjimym3Pciowdp44pSR4DsW2VXOoOhIesuOPB1uIbBzAlhH8nD2J8j36nQ78Z14BvrXxpiG1jjZ/MGfQePdi1D8BOaSZxQud1ZEdVfMoih6kSHDv3uM7MCXB9vk/bKlbVB84qTLzNgilaCwDFd5eZ0ucxkTvZTCwmZPC8cFhoN9BS7uLFoNXPYVEVm0ZgS8HQXR7oA0pslbHbLPQmUBbsvnVYSQZqE80OaJ2UQ3rzEugfVacr2C5fVaqaJzATEkCLJLAm+CxFpmT1ymhCli0fs+6iwB8UtMr3Al4MweEEd+p4oJVT9w4XYxDN1L77kaL2GMt+dWxdXaxszulGGPSS0WNW6gdn4lkKBXfkt6Zke/5LcFUK7yiXMzSa6pcZeL8ttKLbasGPX+SataKj9HhrYXLFswk1SnbYjWx90DWST8OgVmtrKhk+SybqE1CCCyZuT+u+1sTq1md5eNtdREaq1Xm5bQM8e9q+5B4AfToNTjTmmIYTY75912U1u7T6R4wyjBG4MBeXkSokLQWU4LN0/UrjB96ctRCRlI4TyFKYkSRHRST55t731vA9CBVLKCEj6qti6+GIvfaGQ8V0ZE1qM4GkDnA0/eQ4UUMeYwhK7mdh/+D5zDq9bjkpKPbMkKz74Cs5BGRn2pkM+UgslnmvOZpjCq6O40LlJ2GkuOcyl9IxLJ21ojRFx0jB4z/618ffS/7TnnGtaW0/AKk1ZOjGScoWiFywyoCEB5YAQNJMIJNNiYRza4SeWofQ15Xf7uWJmzFh+v4BHimhsBzNjGlgMdwKBeiiq04Wj8Mq67ixicBX+ppUDNLb1t3tPbgAUBJMpY0sBRscHVnIbjNgfiArDqvnstBGrnCr24k2wE0iBJkdbaEyB5EDRkVOBigcMdR2S9QOr2wBGBi1P2FQUbhiE8FWfLGuWM/FCHbKH2nUB5/WQbAxSSVh986Y96DqMArEpX1XtEsN9LcFpd+AzumEI9k5wTAApkdmHmZr98jTG0PqH11BqmsTfVBeisgWWoxmfHAbMHhHrNKwpk/N2sIgbZZ2REjEr5Iu8XAe1UCjWmWhcUt0WmmSIC7leYeZ7d4QkQlwX2BmwZjvBMxpjV9gVb7EVAIy5ZbvG2+ccpzWaUKWra0sOdohygLwAVACYQsHHT/ldnH0jmVeumOZ6k06Y6HuPUFaWPdH/DUEBPsAG/lJ8acKrLqcgcxj31bBEsr6kDmSSr0q2yw1Cdm3RR654prifH9ne83uLUwhywBlDhYHi3ugWZE0Bt71prGBacBt0GRop2PKt0T4fkCkHJcKuX/vweJiq6V9qjAEYXCUQA4KTq4b1Fa9wP6Q1HZGi+lGMNgWqgjJ1AAG81iuXyUCG2a1XaQKe5AZW6UEGZDgOpWGgACvuH7dxCB+bOFR0wS8XFyl/DqLEYNK6jPWtoah4EzkdrPGg3KUMrXNRjREHW2ceaNjWj16LwhUDBGGKAqt9OhnaCkm52hMJdJx2hCAfBymH0G4PrKeW/LgMw5U86DeI/jTVauKUAJ2FoAJ5GwmRse+xcQPQRxzLK414TOFRBKn3gkgGnpxlZ8FcFu4pUZaGhPEIchIEEaZMvauU9jaRxXBnBSTCglI9FRraeWe5jP+BMzTjMb1kwGKjiyMz9H/UwK3nr35K+Udto8/EsB6wwuUrEZIRVnLeg5mYpJ1vQIe8zolQnPiOZBQq1FTplEFQ9Ca5UUXFB9gJwz50PcSIglXcJ/zAIBciYhloLOLKrZBquA32Ilox3rqGtg0vKotEVadBNOjmfG4pqBZCgTrn+QB44xIiE39Iz3ptz3HndwdMqhxMTUE+TBCwBT9Bgi2lga3kbqrpelrn/GVw/h/QZbaCgh8CD9FJDLK5lrvky1Rgov456QlB/pAjwqR7oFEbyzIkR5qsVDk6A/pRSuLt4Yo5p0DTfQD5foQNFj57c515x0haaxq31XgMIT+X3ZkAAeNuthe2e9dDWqsVeSI92GwW4gIPCbyJghtGMc+jFLeuEUrYxp8NQsjlCR35V+9Q1FkeDu/s9aKyrypYXMEx8Lv4DqiJvpDE6l62DXQQQMD7THLzvdaoPoJfDwh7bwrkOUz5WtJNCW9EFRsb9yRNdxgELnQqHBkuho2L3GVjWNK3wVdO8IzIlWHrPGuyBa2pFhx7PqQlP8U1wyWQQe22t7hZ9fxlZKWIalafs7VzGMW6tgRoTKJx9EByNAjPVcy7EQOGoQkMwVJ/YbBQpncJvZS1obPwJj+ikwbzcBs0GOJg0G2CncKnqhnT4hLYWysjoGUojyNNczPmawt+rZEed5oxKM4Na5OvgiYe9SDUu144ABA52tMtCwiTo2hvGE0yBqCZjFg1w5aXXDgpxg6Wcuzvc15g4myMBaThWyk4A8OB58JYGT+UU2jFSET3SyizoXUuHDHnlOhRHBuic55rp/A0n/aLusjh8rSnTiYpKciqKmNqRaYCUac5r8aRJ7RpYC5nms/y7dKuM/0LEbNGkWijl8tFGwo/s9GhKodwaKJgj8mhAhG/yjWFvx7ocCN+4xcKDBw67wNzgh04+vcHo9J1P2hYdBIKaxAp2rYzTMZ9PYQyQLg3Elrazx7irUipc15aiLhqs1axhSOfZ0E2iUZS+7x4EOPHEdBhlo/R7R+RU36Vrec+12rwM/7ZfU4eOv8sJpEzrsxkNmuDjdcj+d7zergOhwhKQwk3CWAAXLoTfVThK3cwtOHkvGvYAAO9UgpCP9lOfbExA1Dv5/1K4vcclP1iDkPx+6eCl++kNdnuTDZfwxLhmhpi4CMbfYyjv0OdlkKMXLRzVNwDBbVYbTkXUBXHNSKoYXisbyFxjjX7xYB3HQZtI7Mzv5/YytgOuyvGopZDNfFoI2utbv4sHTl4q6TrGXFGhQSvlsZ5+t19rAXUUci9sjUjRicVTI08cE5FzPDhON3pMXvgzCxhl3D0r3LCYiU4P9+bAcVyypSz38it/niUxZJ4Cn8N4aP2nIdfnQ2ZZDdWAYPBNhS3H+/P+hkAdOH8QmDgIFAfNZHSHAs6IkwE4EH7Jv1ePqZ57kq2RqmA421g4l1lrz9oLZ7qBHQcP/kgNhniEHtmqR6iuIxV76zCjo7O615IBHYU7GryQCzJ1lmPryG5ltov8aubjlQLoJBJgUJOjfIc3o1GK7Z3NXKnvUl2k9Qs2rqLkFunTZgy6jiDnaVuhHWcgIOPPdQOMdQUcq1IL0am38RS2YhYxGE0xp1e/UXseq+PmZhQioB0ito48QIpbFg0r5cty7Iad0Yogv6xc5y4WHAZO1DHwQC6Ufa2t65b2obxF1HQJBnQGSYIjR/Y/UCSivyDaWy1C7xyQLlRIfg1QXSt/Smm0ACtEr3vVf1AgqJ7fFv/sqAweFfcVmBHvvWbb4skB0bBGvcMshH/L15vXDNSDg0g7B8CsKpat3AqNNtVBMTvljDRdAxVwjCKYs3E+h2Xn932gYr7HcGLmGu1Mj6mYa6HGd4Gq5Ld19EIKHd8p92KzGPdWGE1hwQkAeJ51hUpHQyGLTXRMOnMLnIieJ5gt2KGCJQrBaLrhm3nwhaK+uqol+l08/S8yhOwK4UsSUZxZshJRayg6Kix8Ku4rCt4rovzCKjINkBhBY3IGGsET63JxZVjx5DUbFVXRGWduo/ivcutBT6kAIHnTM12QZKkYDBCOQwWRmwZ0Mdxc/f3R7Drm1QByGHJNwkZulPUdZ96qG53/tH5g+MynRtoH0LOTqwUkHWwTrPKZ8tic6SioeY03z4kf9GjtktFH3Ob6EJnNvoQx4JBbCm3zfXdcEOwkGNlGm2ubqZpp+Ds9UkXHKj41Y1dLEtt44n2onkMySjm14gQg2kfO93ordK41FbrhdFWlG2DDUYy6PWvpsUO56+TzclBBe2gi21DougCBbhBlBAMJ9hMMsK7AlNoRANV3Wk81j6zmtwofo2kKycpC4CYLPHpYGM8YuSFYj2BMXYZtKBrpBh6TrGgN78OalNoC653VLzDF+ZuNckcDFo3uDhYbauvgkIhK39kdHUFDOt7WfZuZxQ8JJmKAg75+upijGLPolrxicbebSoYaInu0fo7zZVFrlXMsEAagu2harX+trY6WB+9fa9p/c3B977JDeKPXW4GB7JuNeujSwRmRoUptu0EAy9LbTyFbxTM68nOHd+9qejyhgh8tAnODJ2fSmkUizoCgdyBMBqiPNYtUcKUlZRyoYHRmQADdZ/3uFSMPHP1eK3Y/ldgaG3QE27DKBiz+5FQNapup520AsS4AWE3MvcjGxiS7mmck/TwOxPLelug7PRXt7Z4nsoVWpkPX2vpXSwyU8TMpzeG5z+rethfR4JpGH4wSlKCAtF2Ci31tG8saxpn0QEUhev2GgkAapgBgTlNmPnY08DAsrpUAQXiGQhccviO9FiGZASJAQ17jDEBK2DQ2H4a+ZkGQUGBxRtCMOzqcdH0NPuBcM6JG1bi3zF56uTIUtSuAfSl808BQGdI7t+WAD49ogmYpWaQXT3WF5Y5LZB+BI4Dyj5wRggj+eAHepa2Lp1Xt+5ut3M/PYeSzMmLTvNm5YnleFrm9LtN+CxFKUUSvB1SAqLMA8mjjcJIkBr9ZqWHWJeScq25pBbYzhriMY2SkAbynjs4Fhbu2pcrrHxy9DhgOhEABDBkab8OfYfuFRPrajjh+SCta2PxTDaUWSh/nVBxh3fSraiIqqggeiXzLmoVQ7r5TEotA4Apm5z2jD2zkyG2jSBBDA1aOhbVRr5MZ+J4TPcnj1s60CURlPLWu4Pi0+wDo6IINHiuuM2NaTeTKL+n0R62BgORJ8zc7lM1VSrHi1NJJ9yBNIPdkmmjgzVLR16mFXLtoPq/7RfNp/83Ppx3SON8UBfD1VmDAVaCQ2gex0tAFFIoKYGm7SRb3KXRnzGHWURIRFbhTtzGF53IeskYzXzkXWGgqToNMnUbOrv2DbWSUlkhlIcYIIYsK65/Cq9Mi1Xik0KN1ZyjVEFBnNkManIAOY8FY1Lz5toyOrGWVDs7DSgx2pDIIADgegHVBLENEbiFMzynK0GXhX3qzWTmevdYdu6tqNaVoQU/QwjkbgAIBk+JnYtw3Irp/f2mu0yiSgY3AIkGcGojEk/vWb3HKKM9PRHrIAgQBZJ2QKCMcXcCWzXHGrJaMZK5FhbRFAB6eVJXtNIRQDqIuHAZ/jNBqKOSbAjT9Kl2i3w3QUcvNY0CBs80xLk/PipXw+coio1hzXEbjaLw3OytiAfvGMQwjyN8OyPt3HJFgYNU0e83aft56nk5/XF6jruW6S5FVzETGX6SPbmRji6AyDH0eyeCKSO8/OHfFBUJaWEadClcHQCXPOOC5eyA3SvQaVr+MtFeqUOBXNf44ReuyOBUl4ycvz/g3PUEFNjpEaDhFAWAVRVCOvOpWFA0YhiwzrEN4ayqrjESeUhkEGSujaWwABfV2cCBCn2dk7KCB9oNyHAbtuElwoHXR44y7RRLMafdRhPWwARbyMq1V82cNgvjdyJXSF4bTTleJiV7nlr7FRMU34p6n3Wf5QVC2xTI0RMHvKPJQjrm+aHd/A+VI1wFTuiWBOQIdKSIvS7aepGZ18xP7j7WT/SuA/2ag4M0PKgKKQbU32YMlL8ZtOgCLCMnksK7cJTBYVNKHAdqGmHm5vCZYBJZ6vKsiI7qblQJ+ZWta1jE1ZtUAoNax2KCWX8QsHtEzzsSvEFX05xEbxSyBVCZSp+HQ6Q0BHTLES+k9KBzWBrGZ18yw1sFtRwwXHhf4OrBxwN1G8YnDVRGMVLbZnCQF2NYtozQnT16d/ro24+yRR5OHIb02YM2cMw+WvNffK9sgZo6RYqlVSlpVCqLGJ5nRAlorTABqFqRv6FRMRHQOF5QLY3TGFEoHDI7lhljco+8LOyI3eMwq+7Cqz8gUFNMGJZxC9Hu8hyGEBBhjHl24gjL+4PPyXIXWCXuz5mHI9NhdVEYmj/lN+ljJimSMAEb5T+5Vr8OJ7JJjlE6SwTA7pbXUF24RDFQUzfIEQh3WU7MNwJx9HAIwHOiCQa7LYp8K9ZQfx+WeysHMagygEejnaD3IzzK0YhczhK8s/jMZB+204OFDpL+H5xkC1RRHjoVISPAeuyIOIL0DQLghLOtalhluEoSSPQEDjRUNDlQeIGPAN3orKDguFYeKCcQ/Ahc5hmRaPuM1gPxcQGVPDZHcdrFNsH7Dd9uDRSdRWpnMm0Ph/n1DFeNq10eOOLcmL65X7lT0pgXHWQ4wOu++TUcvK+YzuIW6IEo3Ud8EOsLal/H31lihSQvU89dzJFpkt7NBXiBwQkVZFXkrFhX55/eX+EL3ErI5SUHZi2pscVKY0/D3v3cj/sbXm3cgrDsGNr3eYxDmhD0oGItgoaqL2ZbT2UkNp++ncYoKSTEsGDp1Sep0NLtYVtGKqXsbOMUgag/0PNZmCu0lP3k8py9QI3iPpbxCFVnNiIDuwH27yvU6Fbc6bZVi5zfU0MR5tKmvS4KhdcCPA4YHxjI55QhgXbgTgTm+pRMQF9wPwG70aBiMNgAyPCJbEQJsciODMlZ4/q5ni953BryA2H0EoQRw3HZGA4bxMyoyhRpF27OB1c146EqgQsAO61O71HuAACFdvQxzKlJBGwWdaw815RkAKF880MYcJ/44TTUaFExUWiFzP116ooNApwBW3GWea/7c5rqRYzGjkU31mBXLNBLbmCI+0IGUG4vezirjqOFnn/qDxtlk9GfBIaNSEcHTNbl8vNYUFOaBN2FHAl3xnknOmLk31pRE07zb2vogM+cPVD1Y7z5RuJcKdkUBkLk2YVa7RMoYDPkHo5Z087VzEzdE9lYBjRSsazLQ65WFnsgPVAsAjsMcYUfZ4Ow9oBTPYA3x76nBjcm6obzB6OiWxm6HnLMAjo1a+BXFV7lYuqeeqigEIzMEGAFwOyLXg3rLI6Oah4GHBQHqhxAWCRroTWvboECvU9enLVVkWMZckaw2loITpTItjbTOudAmoJ4HSVJ64J7bi9L1HVn/8v4Vaesbpz6/b00+CovvtN6dsV419nJwZFfUM0OphKd3QmntCfQmb79mVr/r9eZgIDL3ZgYcpdfGY4rYKhqMGvIqBqYXxi1Ia3hdyQD0rGnc6xMJq4hcXbXkhaOYWWG7U3thokoZssEnfE8Cr2IzlAHKdoDW14aXF3raPge0ApYi58eLbTpt530XsthP+bHkBxuCvmDHhYU6F4Q9QxyXbFl6LIbdrKMZEcBmv3EjjRcAbMRBWLKAtVkrQAUdUuJkxoryUKmeuwA2nsd4bK/9MHakX8GhYUQnALj/bLzJZbeiY3nqdQ1qbfMryrfnDnbn3Jb22oOhad47gWrgpqNZ3dIrOY6sMwgdi+Iwp3AuaXutU/S8xfvR9GkQML3LNCzlsdKI1gUMi6+if9/TKrICYBjF7M7ZyiWMRtuAjEoZPe/Bo7zeEAjPFJJzcZYZjrBMAaYeZwcRAmEFFILeu+X22OR9y2tEmkDyJGPhHhoDtySzDW6G80VK7haxUaEegEXW7islkWnWPLMgtMImQGdM98w0pgxac/KpuE6yV/cDr0DRIZjykyWysN6izrmdmXT8rW2CFQEATqBWzzwdZStDeDndK1uDG043024hherNoc6TJXSW9SzBngIJEFyhlgYxtKBTPj10PEHqQw/WCsgxCcB5wmZmYJj7Vu2CjN4KWEU3NC705wI6a2XtBvurlDYy8bCIQWmzTlUFd6NZfYf0H/J5NuNDbhWKW3rKMMj3ASNFUPi9k+nWAzRPu/+AzxpOsXEezI30gzS4J+py6KA5K3s6w9/1eot2xIM5tLVD4SsNo+M5JCENoryVUKFgclDeKvPeAkCOqN4JSQiid/OqPnekgsjzrkc0IalQ2xuLbjH+GkxZr4iuvC+kxcSHgIBWYhi96mSmQsNDIdu4cxjpVRGNmxmOpTPQJPAUcMuzzYOf5/GnC7ALbD2wgEYcmUVL8E1Izh4FK6DqYJQtYoOd4Q3kKW2eUQjv8Ho1K+HfQNaAzJoDPn7oGCphKknIgOPO4Eff875GoEJwpR8aWHUBWNyBCTBcq78lpczpyRggQ5GZJ2doNyJDmawHyPF7h7JXpBIOGU1NcCoI8bxx2ncSK3oP/qv8uGdkSXcrw8DoRl670b370f8C3OYIdCglQ/9pQ4K0ZoMcZ8IqtLQHh5z3XqaT27yMwXIAWJXaKy8SaRQX5dO4XXZh9XUa1yBRKtr07mPfclyLkb+6d/KkxiKZY/afE099ADi25FTMV+H1TLmsZWxH6+e1G6x0Ch7yHkbgoJ4Ywt2ay0oXN0elW6+MgTh1SNdvDN2h9wzMxzC6SCPdyt/GePn3HIQUXkWe7p4jUFBgdbyvH+m10VTIxjMr8qP36rCv1p81DAAqGI/Q0dPk80ClB4x8IdVEbYlAAsXD9Eileu7z+WM8lrpDY/ACFFpf2ifpfNVScHLJMgbZaRdNpMPqeW3Xzi/jenUdgABVhyOiL62x3N9nPE7UPKmYwGk89aEP9hhyd+K7gRV+x+stIgOBpZ74WKVYVNhxmp8TMUJdwNJjMHrFAWTeTYooWCW9DtJdgCBaz/L76rbmASo4TXumFRRAHXpJRDFjKA5n5SaEVwoIvXBWqzIMVi5KABSSDHdlz/CoqmKpJsARR6PZAkRcWCPwCDuyjmchPSM1GgpDNgfpwiLqTuhceyH0zCkuzK1mQNVi579TSTM9EhHdIOrk4TYNZQ9PztQQSCml13n8pxwa/505OCl13adJTXNu9DQKabeyOY8UWGsWpuoZCXJymRlyroNeKNQETAuMhIoeyxgWVChbdNaz7wRR77NWIXGJBFaAa0QMzCrKH5zjqluPQlUSsliSIfUBMSDOKi+PfJvkC2SIClBc6oiMXml7bYLg5LkVN+4aUJwumCNOmiXu4Pz28GKATAe6MZ3I6JsZtzYCx8oQvWu3TaCighmZUgMu1LNrziBcl+ItguQ6Z3verJ5vDkvZd+VpDexpny1vXICwnjG0ix5tApU9TxmkgBwDVF5ahciJMQK1NSPk7Yp3JF0UYpvvHTDTEctAgT86WfAN3AH4av2ss1WqoZKX8Jr4fjoIg1bFzCNcH+y7keoqDZJZZAZkI4EAOSN3vkzj0NsJUfy66vNA1u0c1G9OR28JnA+9wU7I5QjWmKkjuuZA4wdKYU1jb03905wnGfD07TNtouZzllNd02nOpwa7x3WO4kSt51Cs4ysxvjvuOYue77/zO15v14Gwxj2O/1DbWjH93RAU8uiAOFLh0ErWnmm1gbNUEMtUKEjlHyC45dn0lfftsU1okAZvjT7jHN8y1NYr72+mwtRlyjONYBQVVb6sQ18AFH6aBW4ypTV2dB4oIvK88zCoqAqIKhbMnJEBK8FA7sTI9qGBA1H1FnyKGSMYzmhxVGWwPM3+yQNlUh8ccJ54CIZks8XzquIwrDlvNKI3AgYVDw/DfX6Rtuj7zBTE6xoY2aC5nMumZZQeLGbjoDIt0EU4qlDW3KcwDuqdRrqACrGnzs520RbG2sO8KiMHlALpcI3FjB6yog4VU4QA5ny+ImRHralnCk0mMBpEynNsXWAdUSzd49B583PhTKDBAoErzFZu+wMyVQCDxaZYGKNBuUYet6LWEnCM7FMfyP5NQSdXa9Cd3pjao6JWlAmYIfocf11Xf0dNtra1DuNiZrU1LELvHZoytO05t+3yLA1bNKKsEbKgHDZdXZE1S1oEQZdWaDujKaYoQvP2so5Wyr5E7SMWswCGrBk4LaiB/DL4BoFghKq4VXmSk4HXrTRK0Y7XUC+GeEsLVmsVtcOnJlt7/ySzudBah1Z5yfeSrkzlJPgy6mpDFp7eAhVFlhuRtFr9vdlYafBw3X/AKD27ktPD0MpZVaStvfyhtAo9TAsdI/JNRVQM0uMqQMdx16As8rAwSGVJaZ0eMdbNkX0mJv3n9dOyDuU3pzFvPFjgNRe89vUWWwvtFMFSRsaOmbcvXcg2O/neAenrKDSXVclOxQIudIZwV1kcL9lIwdY+/DwZbdtRzUgSH2we5MNss23czOB2lDfgs4jDgsIc/T7zTkGPoS9toa3FHYanBNzo9bEqVv3vMxKdfccbLOtIaDIzAYAKjWI9pJG2C6pFaRjyYBEAYL5vsdgH1h1wRfxQ7UKCAOzuVOimYrqj57l0rOu4Bc5/v5a1ZGyngA0PfnrvUQz6GqZvWePdVoGl/O4whq7b5GerhIkvMzZPyaI2O3jjm8OOlbuqIqC+BA+ba2IBHAcOFmQFNi72gEemE1RvkCktIkhtQ1JBqwZJRR0zRKvVH4YOAKotNtcrZvRJv7mxdkFhzSocYT8LrvkwTp1eMSrlDbVNcdafWAS7YDpIGBgyR7w51sU5CYcvAsvaMQRgYWXXSxlIgaOar6Xnh0DEDQKVAYIIAQJJ+jDQxYcCuQYogJ/pRkDtYDN9sGDrkqd5GuBxoxOS4CRURwFDxK2OPM4CNIP7hg5Wy9qmwI3j6NSxlKERxgVWLNxIO6v5JHXAZ2ApKigjNPSSCv6qTbrRcKtQsKNYKfC6jrIdylFKSMgfxUleES9M2tqGuiUluwqQCBgA4TcYNjbZ9ELdjUhnJqM1YG+JPCMid2dE2wf2E8jFz9qBg4GNLJpelQrGomMk0Kqfcj617TW5Y09dQtpVUWkAao40AacRDNR2YojNBBzuNZ6UoD29ZtrsuocGvXpdpNNIV2OjhtC4pQBL+PXL1CHjYYF0WnSV0kKjRuZ3vd7i1EIrIziLA6dy00sKSAAAYx6KEgQGsQEoD57hUyqjlQqiy3kYmqThXHxO8m8w+5bXLXrYHqm0c+eSmCOHs0zvA3UOdg2UQztAABCnRe4Q/AQE/JIhv2hMlFBYylezM4JV4Z7ZYrZ0weySRYO2UmDWAVs8ec6ZDzcqXoVtyVvdBXEYZkGx2uNu5/lwjftfw1Mvv9d3Xp/v9fQnCecFUd+JYfTtdM28//igv6OlKdQ+H9oGRH9n8MhKXuN0TyC9d8s2xpY0y+KsQFTxqMH3jcaYMmarOuqlVboTuIgu5Ko98WSp8oYHHWTqBng4qz+lnVSPovJKhvUt0xjyvo5gNpr5b+kOw0irsLAldA35Q8cYIAJXy9MHL2JrFtDpWRmFCEyVczpKNwQuF2scdoKNGs/koaHYLGnxJMChVMcpHOPMnNm4jt5/AAq5y/tPqoHg55Q8G5EK0pzaanFs7CTCJbMGaCDAMAGuoR9PHiPHIz0+Qtjl7zISlEPxilg2r5FI08hxdBkRJBiQQkAMY5Jr3ACF97FJ6DtjVXoFpXs7ktH0KLsIRXdb9urujNJ0aiXTDWsZnL1oVDMmQJBO1NBn1uMpvUdPX7alorWi/0yc6zby6icXqqmRQIHWooz9AG4zjP86vXevxwrkEvmUMznt6eRBjXcqrjsbFV0MHPefFXB4vR6/f71FmuAbbihaN4VLaYmJhsYrAplJ1BLb1aIYUsHkVVn1OgxA3S5QBSpa6zxuEzy9j6Br5RbIAFHrWD99MTCKTyhw9ahC3XouB3MYajvSEOqgAmymmd0OpZStPh+6ANpDnHm0I5sMsXgQdmEhIXKy7RCS6UeeMYbR58mIUnkJjKwbk9wh4ara5Tjvi/1e+ypZu8vf1Y1f/x3e+MlH97UEp6/dAal8T21bgVPY83xZkqSa4rcxtfRnEeGVcqvUVyxZRspxRhHCYmxuuAMDgaKr6C7FFDL0NHaBqYfbqCwqDQvdqo3UvT1psMz8f+T4lWKrIivL42it+FkKRc9KL9+ij7rOw7OCU9Dnsw4jis9kKBd2r4nliOcxxJXHj1LdxSLdiIgRNluI0CE2gMLNFjRIIkTJq9J1WQcQTAlsRiHy+TIYAlB5TWKH2xiPxhG5M6FGa6XRYoAt9UXJKvp0alIkdY6IeCEPDgPWwDSskXCNZ+hAyXQdZ8wJ1/20hks3K94owEDdUA7ByXiNuoGqV2gA268GDI3pmV5lKsX5mfH8DBXGa5iwTK1Um2rq5qhGQg3yzayKsh1t6vIeYE3Q0Lt6SUGMiGIZg6FvZsRTkaayV3fGXX1NTgqlQpPzwSdm7N+1PLVuHf3S2meRM07jtrtxdmVCR3/K+ea1bXPfDATo9RZHGOfCmRafSuCQopRiU6Uox1KRwsFImtLibbM3OhBu2awDk3tk3PMZNraHGIVbVfhGWVCXxyKdSclirFUyb6Uzqi9w/llIsDmz6RAavPUNxx5Y5ZeaeafAauyyoFFhyTy29ABwwToucIY5ow4uAc5bLHlPhv2xPRVKXNHeAdenjkrWPANTMPQqQCBBHbQukC0QFoNO9Y+9lnnPQvRbXq8BAfJkh2g/YfM5zhnhkBF0ra0m4fOwE4KpjG9yRY8y3Omp55bFTc9FvS+qIKqKWZWWuZ+AjGsag3M3vrHTrMBcTzSBLmr8J4fPho5TIagZ1BxpqgQzy1MGl3GHY87Zlme7anayVLV4EPwY753Gaq6JgZQFXN1FvYyrfmxdkFsCGdEz7ZSR4nI0fI+ap0CZCoOTbXM2q4wGwc1KAxMyAsbVoYyp14Qzmpe7aABs5d0zMrdY4rxpMFvNougJykDKSoORiUtyaAQERa+xoCdO5pp7grLAjQzBYmGBDBCEhUEhZ/FBrsvYoXSvG8qcen/BxvNHOuFkSCp9CtYNtB7vWGcDAambHGICAhUA6uwagQCLWWNA3cN0jCOjwiGZ0JrqAZRdj0qC1VjO6GMaoW8yjm20uw5MUhntnJz0pbWTULtmRKvjdFvTHLXOScx6Zl0r2sqWDK9+6uRctkq6k37iUicPDD3xhsr3zXcT3Gun1IBQP+ku3iDSgZQyjY4lUatpUoBsq/QDYHZhp7jGOcoDR2iiUV6HMb+/Vxbk9TZAsH9/HrsZoJwRfehwCu10OGvbnnF+bP23NIF+lqIa+psh0YAelpeu8V2FwoHWHlDdxMHagEuOrXYT6ASzUjMckrgtyPOVgKFyUYSgkf+a3yfDn73wNt7nNpiKlkh8rdYxTsALQwiMy59jOhnpeE0ma0YhTs+e/2qNg2PSA7muVrfhMtlpVe/1cH+dqpIGL70bVk5L2bMGJMwyR023LmroSWMpDuPf4t3uqMFjk6H2xsGDXxi1Kf0bFQSqUHSlJnqe6jI493JHdLtvzdGUgnAd1JMg3GQw3EmfjKJlWOJWsl6Fn/JMRcQQAMpqnszW854AwjNSECu3H6b3uEqklni4V5j/YagXceK9QGCQAQGlddDRiMDQBwn8umaJqcjw2jefLawzEgEWolYxoLoV6olVzV+jYdW8DdswPcnJdJycpYyauUwfC3szMpEeAg1GLFh1TJThEf0YJj4BKckm1yimoScIqqJWb95hdNEqQpCf2UK2vi6cJaeQxYFlQPM/AriuSBRQBZXncwDQtQSQUwcChRO1Bi3vjDbURG2G3K2+1VHdtg2ntSw2JtPUNc3j7QCNb1qCxwrp3483+peIlsHTRYrCMLoUAhzUOV0I2jazbSVo8CcgkxwRwISdp/w7Xm9eMzDSgBLMGZ6o+QEQejm1FVbIcRhEKQ+sVIQG5/Yxbg0CsPfOE/vYzrjsnxlspVfty3Dj87L6maFNRTMOuk5rFOBOplkTCFjbG0y9J4SuD9RN7BhMuvqL2uIiHVF7wtErWw3ex2ll/Kn2xKx9qL7V3NYT1SiE2kdHJVPg579pVOT99rhwzypSZsOoPO2LPS4fVMtlPQtFk3PcY+hR0zOh756/3GB8KLo1P28AcgIBw8oLcpzyyXw/BYbC46iGMR67o1YaqToVArm7A55NedbIt+dE+BkNZKhl98zVskKGijDtSGTjnrVYvEUjqzUq4z5VXdDBi1Licdo2m4ZChzRVBbcAaSiFoBy4M1rAMLc5cr8Y2FJYQFpHSKvlNqCunNWTT+2FuRzrAJYMnECMFLUNlRcFD7Bs4UYwLz5M3mRRGcGM13obnQDxsHjCADsKHMFUobRwsxs2jNuUGWK3XtMVasaU96ktYuKPoeWDWw47BD6SIOSF6lRqgYyIUvmrtswBxC27/KmVr8L42locuVYCBq3LRMIGJiCdaiGwh6xJR1jzS4ECb6AAvidwB+XprfhGhlQnxmb0LLWP+La2HOrQsBQmQQvSMA/VWseoNSnZuTdtQ6fMLpq1VU2y3uv3Wuuogsz5kkUXaZDA0FXUiXu9qHt0FKsjwUqrDeUHoA80s9bvk19UJFqgJlrNoO+d6yzlSF4IXWWw0zi/+fXmYIDH4G5woVURykVbwUOGPBHTERuIG84HM0jY0kAsz5PGDIGHA/C9sIxb7WxXxei2hQtFFZcFZ5HHXitD6LVFigx1WbgCWSG9AjdzoBoCUZnPcJD1QgmdljcixBky2lq8okwCAxltXVtKE2iDlNW6dcaCGVRdHLAsEqxTzgBcLoNBpKQNuPWRyCGjA0PgCuCai+8b2Fdg3xD7lmPaq5TKRJ2o32Y+uYfR+XfrsgfydCvKpzigjBIAsO8CFNZ74v3rsrO0ZtfLIwvJ1qwlIDh0GkyzQsnVUdFWdiPkHJQ2SH1nqXCcod7DcItsV1xeKwV/BbIVdBUV3ar4J+LWIFXrXl0Go/LwPgy1+Uaed0dDp9wWtJ5Gfg7sUdNQkQUpow2EOZY54AciDvaeeollFwLqjdgGpddW+LnHBwDcAEXUA4atvLJfU67XAb8lOF32Ermdb8EcuJj1jqCVeywSxC0AF3rMgdiOOFpZLirb5CkB3EwTOnXJDVn93/jB2e2PIApZz5FAMI250wvr9HDm+90W+xk4FrvaO27VHTSWwHV6tAHP9VaXUxgjvN78C4EG1RAELFY1OVph1bQI7oz489rlCLW0lmxjAXiWf/uVxj91VcmnnA63O9lHLyIAxIE8n4SLW95+0NDfhqDKoFgaXWx+rnqtYHST3r2weQS2UX5WYHkWrkbu9aLcGG5BncqC7mV5kmWm74CrRyc4tB7sMbGMkThMPWypTzg06YNcKDlNdgZj+udOv/R3/fxZ3bwQJXUF+U47PgCUx2j8zuYpoTaeV7abzzlU/8VbcKdSKlVn1LmrVF6vXMHIYTbaC3MoF7OWYR3A9t126Xe83rxmIFBsb+hCvXBn+lzIzosIVkJCmlD5VG7NaBiZH8vCp4wKCNBjGR6MmTwDbhm/RSyelCV7irEgJHA27xlokO5jqtThxVf+JhBHM0ScVkBMwZCccnqKDqSEQ2mPyUSdajho0Ii4Oe40ZlSU8nCstNxpDfI2DJ/eFA1QPjAKLKrzXh1GEwpc3s3pyTLHN33U/M4phd6kQT/vLuB7MnLD6AdpVbnAMRo1oFHIP/9UCuAMYLLASPQTfe9bl8ozX1x75V+NtvteUKLCxRXFGkCDlwDslaHjqQ1W260C4+ClmFXiVkr0vnrbYoh+dAoluWFT/pzRzATgB1htbwfygJcr9j5geJYgIm4wewBiY60rgJV40A2GG43aQhw8T8Bo4MMTEjjgsfC4X8JsweKA2QVbvYB4KBFPxMjDigTMVIRmaSCxIvsLUG5l8FVaoFc3IIra+hcw7FNkiQV5OoAC6Xll906k7im9gCoYtAU2son+vt2aJ4uxFusPFsG7xsjCRV5TaodertuqjO2hYmCBFqTxrmJB1WgY66C4toCUdwxhA6SjQoVRLl5CR8IxJ01/3B3qu5CpWtYOVbSADygvuNMDhZzzq2WH8yPCHxlYIySx1PqV7iorOPWpwDuhsHUH2tR908anUyUP/9zz5JwCHMTqfP55CDgZiPGM03txf73+jvsPnz7bMCLNdl5CHaUO6SERVQ4zW8lT7VfPhzkOAFWLE4YLMlF9ZRpPfLkQODy70jzZ6fQNr7coIEwFl90CVzFANtTqAadTpc6DOWnljVJom3kZZwOAQoyptD295JVIMQzZHMfaSC4sbMuQU7DiOKw9yzClEFCL3YUo9yEhoU5MyRprMIy5XssANQASUqT/plcUONHzQRTdJzBCjL/SI42qCr4bIt2dABL9gb3FFz0WeXPhyAI1htbAKnGJ4rGmJR+LMef5TcyO0hG6iifnjtudvX15ZwAS9Y9b12fySFzKREJvmK2G8++ngGD8QYMvwEc1bCsNnQGmhk1DkQKAReBYSmspu2w1HyveIa9W7pZ5eCPvM6xXrbMFIuh9GXPbM+dZoexivOn1jG1g1go1YLgp548M7661kjdW7j3Pwq1X8LA8nMgdawOwhR0X0jkBRayFZ+uSICEyInHFhnl6p2EB91RWanEcAWxxVjDYK8yzZKQz57wYWZuAQIpP792/FiftpsZC6SZkgSTXkHRLPdAgwBVWpZVZFnAsrHBkqySvfHUvT0dQgEWML4XtzXsBAo8ox0ZRzICAg9X8eWdUXh7km9q6ckHBP7siU488Z+SOGjpKu3Lc5PPEXpcGEvC+ZmwjtDL4mgv1kN/qmi6s0UmcHLuP39Hz1emoy87FfA7Ulm6BhqoRk7GLLu40XuuVQsjFZKLzJCIF4g3jiePXNcC2nKpAGcaTnuJyFxjhZV3gWDfCQspGO4a14OOyYDh/97rqUVYTQKVi5gFWAdaezQm1nez3Nm3eYv2Rc6s9XdQIHKEo/Ju93qoDoc2fyPxgdu8iU8qYUg1L4OsaLroXkuyBLhjsmUL5GdbT2d1YmRrQe2ZHdmznew56ZZZ9yDNqEbgRQOdD5lkKMt4xFpK/i3oW6AIRahh678S+6JoBNe1h1MCsPL1ETKJeZBpQDbhV0Vv4xJgzVBb3fhnj/O8S0Tl2AQAVDo48dmwVCXW4rcLmWt/SLzK6Y+1lQIncnfdwebFWt63rzykAFdz46X1bB6rJzGtCeE+3GArszb/GM8WLBIxdlAToKGXjZE9iJjKeHscjWEWjGWWx9Gi2wsBgMZXzwB4ZfIVIq9oeOG3L01xoVTi74h+PwEFZ0tZHMJ0RlmFy3DzPmd8OjxsWbrhg4Xp7AOB4thyfvFj45EXg2x89x/vPDsQt8OrqeIUDf/nFxl/+5hGv9tfYeyuvggse4HEFIo8hPtbCvm0cLNxFyDCnVKhbIRBqZMk135lWgFIrWu+8Rx2iVFQhqKNn5GjFX1nruU4EAtUBzoZ5EJ+T5/rUVwG/1FYqcFvkjSzakwdHpb4izwGjXtm8/7H6ZEwl+SIi0wUreQIHj76uwVny0+K9nXNcQyKDBlnfc1FpOi/jUtV7lE4zznMaRhQPV+ogAnmWiZAN+v26eREUsF7rdvOm2eLJtrZGzFVikmmbgNrbS6+AUQ9v8gjUK60lRUNSrDodcUyv3roHl1Zg5ESLYbMw+If4dsxd9UncGkkHpvOk/JKaJKgwUPdBAL6o+5E6enH3hGvtxpiwoILOCsmIhxmqciBT4EjD/3BKJzYt1ikK+c2vNwYDC6PXu83CQdYKjIrm3ONMBWnaWiIZ4HBZYZ+2Nxc4Lun1u/GoEobEHuxAWEYC3BbWccEGcEH+vWHsDMaRMvUg+UqZ6G03ak+aDNfotxaxKDkX2mB2tBIyaoT592A447aeRvyc7DJoS5U6tHVRFhmglEaO52Q6ThiBQowbf4KFhLuE3UrjGbWY7mGVp+45vs74jkchFXSsOQbUWQ9d9CaFNJWSlEc/owvCxBzNxA1QWgiMoczalnkX3pOzXi1nIUEULQlyVn4QNCpVpsexq14kn8oIQbRHH9Hd+NSJUAbUhsdnsNodk0ZJlfEjwlSaEPV5jth6zlUJ3WAku0/nc7bfMkrGQdv1wAfPF777HeAnn32IP/jsW/jD7/8Qf/D9n+LTb30X7z1/H4/Xr/H5Vy/x9XXjL371c/zzv/gzvPz1K3z+1Zf4R7/6Bf7Rzz/H57++4eV2mD0D8IjYL2nQD3YZxIlOYrWaWRgwioZlNmazsZSXQ3cQRUpJ3ztdlT4QTbiPuBzloU+kWUVV6VjYyqOWkbUP3m4hZN5GLK3vY5mH1egXQGytSE70mD27XOb7O1MzrFVSLUrVIFW+ekSRqiCs9Uouv6pgYhgyvlxhZo3Q+rsTNQRQNQP19dwdU8WFE1DYvWE6r3N+vkuvFB/UvaUX+aTgilgCXQANKu2Atiz6HHvJO+8XFaiYSzTGdQZERk9+cMKZfo0iOPbgnBnNtOaGU8HmfBXi0SOmDgSgQ9H4PANBQUCWiV/TfpxAnXCKoEPB3V8xuNay7c0DjLpHO0Z67G/yenMwQMSp6tBMxesha9CUxlaG3hjqXVqQA4uhWltZKBEsJFhmGS5fi0o5f25MCaRXeqnqoBuA7BWgDIzqAMjAs90o308mVDVyaY+xYPpbeUUKYxiiDh/Je5yIPHgvAOYH7eSZdrgqstgDC7l3d1WYsXcp5FIb7CTvUrxGUAZGXUJtSSXM46eq/L2NTBu6iaDnZ0POKiRmwKE61qgLCncOw93fO79Oxn14bAUQ0Ebh9D0epJP0ldCA1fyrAyRLqaKZbpBnaXUvhVZnKieQnp8dDU6kL6K0hCrvBXycRVMGHXoMoPu6eLAuSNoqrzy9NHitdRX49bqozsAgoJH7/29+g68F8wes6xWfvf+I/8FPP8bf/cO/iX/nb/zb+Mn3foxPP/kULz5+D+999Ax4OAB7xMtXv8HLx0fEeoaXL/86js//Do7HA8dj4F/94l/gH/yTf4j/0//r/4n/4k9/jl/+5SOO9YCwzDcHZDQTPFXgy1ivb8YIkLatWgFn0w4KFvEhMrKngkwVsGrf9Entcr1lXIoHuEjE370GVpxK/ZGhVGdde3r3Sl2ukh8zpiW1KwSSfRV3RulYrZyApaRpAVieqYg83CcHlu0fNF/Krok+xkI9544VjV6RAFMRzZAzySEBkpjGmDKI1EMnlgtkGF2DXzmw6rVhWsPJm0EVYAUolDZR34A2pHbSm7MuJ0jfylRGQK2Z1S1Tx9pLcdqawExMcY5e6pCfU03foFMoCtLc0XSbr9/qDI17S6dWXiz6lgPs1hxtpIpoB4IdSss88Mdp4a3WU86Kl3kyLqGiocndqofiScDslfKGWOBtthamUTnkFVnuGVZpR9EGqLPRlwHHcQGWZQdSY6iYZ3PH4o+pcv9AHImsDxg2WAC/KTzrgrDcKeAiKGg85sJqXz7fKgZFjMVW2VO0wKC+AoUqSxAMgN3GqgUQ44xxV+6HCr9yoK4bFtiEck4qedYugTVZAulBLhVjAVaHDy1EXAFs5JG7zFurZkAhvh1VbZ9DqHZOAKiYRsEkxme1bYi/F5onbdqQE59KMEgbycs0+E97CzBiNO691sEisuZgCVQxvip1AygNXDZdfDEqWUZtiqqQFxaWLbi1wV/jsly6qOtzu2F3aehTHaPWBB5c0lSQC6tSl36woJMTckYKgDwMKGuHEgSaFK3fcLEDN2xcDLiwf/51P0dgY102PrIHPFsOX1/j+9/Z+J/+yd/A/+zv/H380R/+MT783vuwyxXXx68Rt0ds+zyB/PVfY7/8OVYcWA+fAM9f4NWHX+CDhw/w3vMP8On/8N/F3/z3/sf4n/zxP8Z/8l/87/C/+c//Af7b/+4Rdizc7JE7hgyBDQ+GTf2GWJn7PixVksVGxA2G9xB2g/byr+UwXHi6qEGV46kborg1eB8o3cKe9Wd+pRGKkWcGiucrW87PzsDVceCS5bdxY5FjRiXrsL9yDpSgkBFEbR+tLYue5ztkYWS6M7536jQY4hapwXnCXxmS4jkDNo3cbSf4mgXKM/xR3D0iiQU4HbAbtD17Ke/ugSpylowxvz23nxUGMBGOUQHk97XTRduVZY5S7waBMPWa2RMcgtX0sSObEXkoUks6qAAVAHCMcDsK9+RTCRgK+HUacNU7vJc85eIOFN3OPU2swEZnWFK3NZkaAKUivauLk0ZTVDACgS54lWO2wnAsZYC4fp7rclgW1TsymrQEUAO4IHBZXbOSXLC4iYK7NWjfLBqM/bbXm0cGRt6/MJZp8iK48l6p1G0daRTXYjQvQ4LLDjLFwj4yLWAM38ViCw4B4KOLBx3AXqaAVoVFFeot5lWJMhd+oj3jZ3U86nxfSzURNm9Ri6g3Rv640H3BIVFIi0C6yWAW8xoNQGG9yp82k2JIAZppJfdVK6DxjRwgmDJwL1Cgq9TZrfWQ1W1nSE/PTPrOlkUxvrZqXhOGTjBxprWEKlrh8jm+s0FNp2tQAl8hf3kF66wWbXWNgDphVn8KPli54fzJMVc1Mzx50YYgU0Mtni4ZHhXqSwBxwG95Fka2FGWazBaWZ/osm+6oyiRy2xXHGCo2igvCgCNewjxTXZmTdjyshdgbVwTef8/w/Wdf4VsfBX7w2Uf47nffx0cfvIdnCPy7P/z38ff++O/jwx8/x/HCMsSPC2xdYOsl9u1r4OsvcP38c+Dl1zieGxAXHGvjvVcG31/Cj+d4dfs11sfv4w/+3r+F//AP/lf4gx/9J/iP/8//e/xf/utfIvb72McrYAdWHFAdQPb9Z0TLH5nWe4HAxrEclwUgbvBwxF5YD4B70sksEkyvQBblBkpBl9hN7xKwFahOb8OY2uDj1P0GmLMleepZxgUAQUY7p+PVrKXUR8k1CPzEOyjD35qhdVLyDcPiEag9nCpEtnPRo4xnH3UtHrxRyclahtxHZOFYgtutbXmWhi9POrSqUzwK3BzYoTMtMgWcE06HpuguIETjJB1BDQZFfpOubVRziNIVharGPxLcDtobZbsjjkZR55zVK2OMof7VdaL/WDypntJKfK7u371yVj1bl+iQqrIhZfRq1IMasxCYPCNa1GN3DcaWtBqjUUi7E4ECMYqILAKfZRmpWsgW+6pmyjh6DnxRD2f0ap/N1u94vRUYKKGLRu+pk6PsmZQvDoIBW8iOI6uY/8bKbqzFk8qORP3muBmRNhfMLbBZJOgsmqqCsBNSRjOvtsbIZQy9p+NM9+maWaE+UwrFvfMV9R8aXKtvtsKQsvCmGaJocGKwAHTioGok+ll2/n7lo6QognNSNOD8r512F0Rvi4LIZYWf5nvzXx8MrWnmfluG84qR40Sqe/6zcc9qyjFNeWj5DDNRkJdJARAZo5XCPXCp70jYWBtS60qvxAbwMtJyseAJJ8XSRqAcJPE+DG4OP/ZJ+KU3w275vDVAHBZsXQG/ALhhBTcAHFekMs1tgoZnMOzcs7xv+OSDG3703Rf44Xc/xXe//QF++O0P8fuffooffPJ7eO/Fx/jwg+/gRz/6m3j+3Qds24jIXn+Hv4Rffw08/gaX69fYX/wl/MuNw58D6xH4+msclytulwc87kesR8N79ikMC6/Wgcvvf4j/4D/4+/jh97+H/+iT/y3+j//ZP8LLLxfeew48fx54/uyKZ8cFD5cHPHu2EXjE7brw8rrw1auXePm44LcH+NqI44bLWjhwQVwdWA/YuOFBKQJYg0RLA8fFrCUQ+WMwjbz+01YzFTnC4VWER3uvCGUEI2xtlDISTv6O5j/xb5/NMEB9hYiHMaRTHwhgOzL/RM9UVc2KYk0ZB5CRgCFB5YF2ZCn7/rfmQZyNUebLpH8UrUl5sLUZ+eC4lxwSRheTbHevHHNvrZQNWFDraz1XOW0NVX07Wj128fJJ0qmMJg4Bhp6R3gRQ0YgT5VpfDpVewL6iC9aOQN5JNqKBhqlmwxvYlb4eSxP1sB6LyT7WGAIWWeQrRlMr63Sys5fI3ImU/tyNTi+PwvE8hOyyFlt6z9UZwsG5Lc37STHl619vcWohBqIyIl4KlwAAf9blQLCVbtglPbYjQcE245ZA0Is7sNfBHQGBzbq82s4FsCGaIYSq5flDYKCWpUjDkZEuG7mfmN+LkSLAQoVvylCP2w2m6mfxzXqkogB3Y5FwltcMHrxh47sLKm4c0jLm0ItbIUFxekQZ+1PKA9kdD7Fh2ldMYJFGsXPuxorerqlIMzcCWhVFyPtL2gD2KS0m1vjjCa2s7ial2hEOrSGV67Jq7vPE4BvXdAAWjU/Cx075Y0sT15WFZoZV85f4aqRuVpXONeJSuAy7MVxaPQQic3NSL8sMxjazRW9EZmMiO5iFP8PCJRmbXf72djzDkUWBcPhxxfaFZVf86DvP8Lf/+vfwRz/+AX7ynR/je9/6GJ9+92N8+K1v4VsffBvPj49h77/A8xcH4I51XLDia+D6G+Dxa6xXXwGPX8Ifv0S8fIRF9ruIG7D8FY4DsPUIvz6D4XPEWliHw3CD+dfYzz/Gn/zdv4dvf/hdfPzwv8Y///Of4Qff+RiffPwxPnjxAd57eB/vPXyE55cX2Hvj5eNX+PzlL/HzX/0c//wXn+Of/fwVfvaLwPX2LNOHl+wquuMVzC693tzylsapd3JMkTBD1Q14UZ08UJaSa+cBX+mN6doC6fS+jUVx3cUwv7yA9NolFTF5GgwFU2lP1aO/LaCce4aBfYiEcvnUG7PYtsIE854qOFD9T+SuNBaRqZVKzkWGHRllVUSK6a4Ng116Z4ciETqnQ/KUz2ugJUPYxj0QpY/6CPR6neoE+JbmR6sc5qf1a12gSN74/WRcBzN8w+t+OJpr9z3hf+6ACmjnzvqJ71dBAsYXeD0/kKZsXZfXHwaEedUBbHY07ez0lsXCQjbeElAqzx8EGRFI4ZWNo261Pquh0FREtlh/g9ebby2ssFR7ZWbB/tNGD9/K6Nt6ICBYiCO33+0jjd4FF8RhuC3giowUbKH6Yw1ZUK4AjKyt8+JQ6AbcupNMUOCkxJlbL4mNM7BQJOFkcGvafDXQqIr10FiHQdccVjD8uVIBc+uQuVHZDaNv4/fgfOtmOpeBc6YyqPPMY2d7XB904E9XwMvTUsX/NwgdLMO5GDTnWqza1ypD2aG4KjoCuh4CIqNEpA1tAQORN8BiJNTYqqCMz5nhST1Ty2OMOE1oYkwxFdgi6DHu5VXBkpno1CmFvLayzh2x4vgMViXNDjXbPmDxmGG6G1hFzuY8FrCL4cGu2P4VAs8BONa6weI5rgAuR6bK9i3w0SXwRz/5BP+jP/oO/ugnfwt/6wd/A9/+9CO89/0PEe8v+Lpg2Qu8h/fwHABePSLWA27XV3D/Dez2a/irL4DrDXZ9Bb9+BV8HjocF7BvgB9bKFVkvDe/FFds3tn2dSu14Bn/5Iezjja/tOf7g3/sJ/hfv/S/x5//iv8G3PvopPv7Wx3h4sXA8PMNxPANWYO8rblfg8eUrfPHrX+HP/uU/xT/80/8K/82f/RP845894i/+9ca/fulAfJBhzw3YsrFNjAVp0MmNMhotC1nQpzXJfxVbgMnkGzw2m/ukjfTIrZfK8YpxnHeqgsAIDJNa10YEdLStU7TVXrh1gXSGUSWk4V4sdO6qfYMOJhoCRD70FogBQkq7+NwtlOM4kGe0AOy2uAKxAxcAx3EgPCNQbgHbGeFYi1vkFvu0LG5XNekGa1kW0Cr5ALYis6z1OKc2DGp/rchYa2aCjBC4bznOz2TwJkAQBcYz5mevwQUnEKG1QdcXtLNobejrYpxAggFZP7RZy1IOTD+tdmIEoDMuU9PS5mxnWiw1RrDTYE4n1/7guhuf8TBCHB4bsRar3bIzqkDEWVejQMl5e+Rvf71FZEChGvF6IpB9JPOYGY7jAjuOVL48dCeOA3Ec8HXgaoDbgatd4JeFbWB/gMXh92JpbyazJkXw/GcY3Tp3UIszc4Ep9OUHhgxUG91Ew+tJ8V7fswsRG6UwtaE+4TU0AQyc0SaQY6lc3Dls0zhjjqEIjd4/L4CilqFiPkedKBbB3+f9BR6m4ESDgfuXMRzIEHsvOu+lsUYrQQh9Wv3nTpCLiPVMqwYpqDlWHYP+o/BvDM/b+n6GVfUjpwIy7f22BF1T1CeQUPypeocvJA43k/nPaTAype1/4aNo1jaOcLyA4YOHG771HvDZR8/w6QfP8eLFA7ADj/uKX331Ej/7y0f8+RfA3g/ppcQC/BkWDO89u+LhAJ4/GD756MDf+L3n+OOf/hh/8tf/Hfy1n/5NvPjsA9w+eISvRzyz93Cx53hhz7Euz/BojvXqJY7b11j7a+DxC/irV7B9BfwK3F4hbhsXuwDLEXFwt9gBxw1x2TBfwPUCvPoSOBbs8gzLvsT1yysuzx+wV+B7f/QpPvvp38VxOC4PF2y/pQcaLxEwPDjwLID3LfCtVx/i9374x/jxj3+AP/zTf4Y//fmf4V/84l/gn/z8F/inP/sav/jXF9ziIbsCOnCo5oOAS33qA2ARLZNI7l37QWcgm5ahI0EM74rnHZ7KVP+z9LwsWH8UQLaPBpjpR0fbUIA6xTJK9JJNA1U4J0DsKJ0SvgEBTqcEVOMfGqIWCsCvUEqixcfo9yT4N7Zxlw3eAvBmQFwy37wChgfoQK1lhoOtpXNLv8EObePWdtdsid7gWcKmKEbKdDkmUD2R9K+IEkNftgxDY5aRlkorzSDdsaBUQ5030QKMihLGeO4EI8RZ0HJKkOul+Ul/xfnzcgbGSwse4jtFRgaWKGpxRuGscWZdWThspdGvAlmohs2gGoYktaCuxpY0d6Y5s9FTYDbhsrCUI8QAY/+G0wTp6Imq7BhmOkAo6wPicslUwDK4HTiOBAOv1iUbBi2Dr4VHW9xFIG4Yp3IBLIABmvLHYDZdBArhCM8HkPk2VneOsFq+bPw7POPT+2gjVrjBGqXq8wkZ6/5KPYjRJ3jhON2Bw4sxsJ4BnmCkvAsx6hoGM8Q4UiZDAIJzrcOLQICwk8l2jikG+g0RjAw5DeMsFirjqkKvkBFto1rXac3uBMOeCGFeb+jhAuBJcU+/p3zhjBLU5zT6WuZjdY1AiqBxx0SDDpj01MGaIAoa8mS9Pu0rcFiG/X2txJa+cUTg+bMD7733iI9eHHi+LvjoAfj2iwt+9PFn+Okn38ePP/s9fPujz/D++x8A5rjdbvj8q8/xp3/xZ/jPf/b/wT/4pz/Hz3+18P7lgo8/cLz/MfC99z/C848u+PCD9/DZiwf8wWc/xd/+0b+F3/9rv4/3v/0h9sMj/PYFHvw9vHf5EM/sI9jxAnG94oJHYF/h+2v49QvY41dY2wG/Yd8esa8B+AXHJdfXD24RXgeWLez1CNXR+DbEzeDHNbsOvnwJXzfcwnG89xHwbMHjmjsIdv6LfYPf8pS68Bt23BDxCv7eFZ/+MPBvf/YH+Guf/3V88Rcv8fNf/HP8P/67/yv+y//2Z/hnf/4K1x24boPHBTsORFwAXBDHxiWA5c/gxyYAXtk/ZN9wWc8pqhsrLvCqoUEWHEdUvjc2EwWW4W1FvgzICIV4WxIWCTzhavPa/GvTABmbWwWl3/tzC4N5euPB9tVZhMuoG4tcI6xBSIBpppZShHXUyjfWqS4oh5E1VazhsoDFgbUZHameaIspAyCQTpvksLdLsy6r1IOVrJT8is7Si6AuKzGTvqnhD7wzi5aXKixLpgtInICInf7p16zJGnZA6vlA6eapWxtUSI/qWUN3tcrI7+lLpdejr59qULcLTpxrmWNoG7agaO1mWtxHFsLY4iCacOo3oIZj8ALCVotlWH6DjgpHEAi8GRZ4myOMExmmMs1OSlmcdSCOC6MABl9ZAxBrYa1sDvS4gDgu2RRBIICKXFmS3JN8sJfP5noK8dDzU0GcIRt6zFxIGWudv668z31Lz4H4DCf0KvTVf4/mMOXNi8P5n2J+/RvnzwFUQVABi/H7fN0zu8AKgUk3BBnPilEYyPl2ac4sdpKBX+f5l3fSldhNGyLL+oyeu7VAnz2moVxOKH2AK3AdgeaDQb3TNsAiwwQC/V6tl80xWambjOC38Of5FowsyCNcUs78PRZ8paLzbUnHdeBh3/DheoZPPjR88m3g+99+ju+9+BQfv3iOD589xw8/+C6+9+Hv43vf+T188ukn+PTTb+HZiwtweYSZp9cdB/7kq7+DP/kXf47/8v/9n+H/9s/+K3z5CHz6/qf48Fvv4cMPPsB34vv4NN7Dj779I/zwD74FfPYlnn3wOfw3X+LZeoEXzz/CYwS2AX5cYXYDjkfgumF7I159Bbt9yXMrNkBDvcywnj0A68qtgdy6xC5oyw+Gig9crwFcH9OTtSNB5devEM9+DdjCeng/6xqvX8OuX2B5pqhWvMQtXmHfXgK4Yt9u8HXAnr+HFx+8wMOHH+OzH3wbP/nqr+EHP/oJfu/3/lP8f3/2M/zmi8CXr274+tXGVy83fvPlI37z5St88eoBr2DAumJ5YB3IrXt+YAXwar1CxHM8x4Lb1wi/1K4AnjF0ksVqQSwxHNr+xHEB8nmntKRF0uSolgADpAsNxDB82vrVclxAQGcSxAC8BAR1kqZ2ODHgZxFZU8K04CrvD1h2pFG1YAsUA/CAtTKCGWz2pg6ubhdUEaBlrYytC0W+AfvJQ27hA9TW+37XliKVd4Tt6OxdQfKsj+j2kKcvx/i99euIBupzOS5gNPikW54gib7n6dZDl5r+jqHardO+5blEgwep98hVNx441N05oeAtuujYsJR7NctDs4YdyaBFj9+q7u1uvMuBI4vuYZZbCu/X47e83ipNYJaFgLkVMI04jgv88pBbqWB4PBb8eMhT3taR5wMcCzgO3CwLBSMUlkfuOIDxVDrjwUIK3Sq3DTJanZCSYbBbF9aU8VL1vG8qw2GUEG3URVzus0/GmosONNqcQGCmDbRgY2sfi0TqFob6TqkQnUWurULK2TdkRYXoofGkgc98oWoppHLOYER7f+t5hnOoyNrAuyk0m38v0sGKATkWCR+/X4wNyUBnWQHVlUQBCknLrAhuuVfr6qGcT8wvhXkee70CGXJPDV9jzE6EqXQN3UY0owFHFuaYcKQhmJPLJl5Z0PdsGT54ccMPv/MefvL9j/DDT9/Hd99/H9958R18+/338OLFc3z84ffxe9/+Kd7/+MDlw429HG6PuO6vgcjq+RUBWze8eBH425/9FH/405/g3/+nfxv/8M//MZYDH3/8Lfzg4bv4g48/w8fffcC//uDnePXwC8SXV3z5m6/wrfc/xvOHA8ez9/H8eIDfPkd8/Us8fnFDrAc8O57huT8iHn+Te5LjBvNH+H5EnrvwHDgOxPEI8wPh2YsjK+c3YhkOewBiYXuC8SOQHT7dsL++IvYXGYbEwnsPH+DVqy/hr34Ne/USe0ca5OsX8OuC+QXHg+N4lgD9MMfFv8LteIVnHzv+6I9+H9/95H+OP/79P8Mvv/wcX99e4svrV/jy1Vf41Rdf4S9+/QX+5V/+Bp//euPl40t8+dLw+aPj1aPh5s+BZweeYeOVf4lXK/Onbt4BM6RcSaRyZx69adaHGAJeKqGjQanbHfPUcTkXxc0yktGfIaiAkd+XpVrTVoQkWZIjOeVpLqrV0US84mBZFOyOOr3SvQrQsk3JwSgDw8grT4F1rGxitA7EulCGlH5hTwVckMfaeFEiQUJZ86E/9K/KjHP8qTS8aHK6Vu6yLxRhrS8Qfeb1whaFuoZSvXMXTtfkfai7Z3RhOihzLqcH85rZqGkivxMdGgylk8HfmSfSHbO8JcrzB3L9F6+y2teqokyOZFyvXzcP+Fo206fGlIN0srVj9joM9A2vNwYDBw8EwjpSgXKngF8OMloWah0EAdsO2HFhQ72s2N92SSDAGoGUJ4ECS8O8mixlVB1pqJe2AWb+DzRaVXACR50RLgoWyuUz0Z5ifzYNKip0lxKmsYojZJiGAY5AtQNW7qme3eGr3jet8SniQYPrpM1SREKtkqeG4zwx0OPgd41f9NXui3XYuJDpncXKqkEHKN9U8+y5hPhgzL8Mc8mJBISH2ehu5ZF1/YfATzMsleKU1RlZUPGNPIARMah1rCl27Umugtaid07kmA3OEGgenxt4z4Dnh+Hbnxz43ifADz77BH/js0/x177/Q/zet76FTz/6CM/ef8DDswccD8CzF8/x/MMr/GJ4ZQcOj+x0dzzDCs8zA+h5OQC3R1w+fcDf+uCP8eMf/hh//vkv8fF7n+Bb3/0M69mv8MVXv8TLz3+J5+859stf4cOXD/jNL3+Dx/f/FV58/D2sDz7C8f4HMPsQz287Me8OPN4eAX9kIekV5o8wDxzrGY/DdqRAGnT++3E8cDVumdZzw3owhC9sbO6WuCD8FdbtPdh1Y68vcNgLvPfeJ/j6q5/BH7+Ab+B6veIhXsAseBjRJbvwXV/hiAVbL4HHhbg54v2Fb//++/jwW38LP7y+xO22cbsGXr284tV149Vt4+LAbV/x8vYKv/nFL/HP//Rn+L//2Z/hv/7lr/CXnz/g5guXtXrXgC96VXpNwKr9BJ6OQgFAGW2qZvLWAYb7JWJR1UVgHuJkGASGI+Zzg7hduo66zajb6MQ0bubplBXpy/8ourcQOXagIgJ5C6s+LXpKIHtABGUhLM9vSTHI/g7Z62xRbuR0DX3AVHA7LNMSSpdpYgJHNgzxeR0A68Mb8PRWeYnh9K17MFFyO66100KMX6LHdHfvvsbO41F4PVDF3pXnqFHFaUoVbCUNVj1TSYkGSDINAqq5flW5xCe8DnnNl+pNuje29KANsie/nrcg/rbXW4CBhW2LZ5Jny+BYF6zLkVsh1oIfB2AX+HFgHxfADh4WdMDwkKLESci2qoWrqbJ1hGdKsR/cckRCZHogPeRQ8x+PDImiu0FVTcLJZo5VnLlAhcTR+fMWjFFZe09ZgRD9W6szPPHqFzBAAgVJpy6mlFBSpoBNMDA3/6qngAkY8NkENsn3PAmRZyooyrIsQ3xrrXpW5lc1zny/ZP11MjfBkXdBUUc3WiGkZ85cbXMvFDI8yygb7hAoKQoxIwpjEK2ENEaePCj6ZbEY6pm1Bc2Med4sdrXI7ZgvAPzoOwd+8r0X+NEPPsX3vv0hfvDpp/jxJ5/i977zPXzy0bdwvOfYx0tkr3GDxw1X/wKX+BjABR6OdXuF5Y5lD4Cv3NoVDjuOPFfDrzjeM6zPDN//4DN89N4n+Pr4NfyLX+D263+JF/6IePkV4tXXwNp4wHO8/Fdf4fjLv8Dlg99gf/YxLp99G/vZR0AAz5fhenwKOz7Eevkr7MdfJ3/ZA+x4jnXJmma/PWPnP1btR4LR5+v90lnPj51AYHke/IUDOzb2q4DHFWaO6zJcr6/w+ecPuPh3cXn+AF8L17ji/8fan/XakmT5ndhvmZm7773PufONMSMyMrMys7IG1UiyurpYJEG1WqLQarQkotWvehYg6DPoKwh6E/Sk1oMgQICghqDhoZstzgCLTXY1i8WsysoxMmO4ce895+y93d3Mlh6WmbntE1FVkQQ988Y5Z293cxvX+q9Z5IToCacny36XHZoTGY/GBeFT0rzA1VPy4ytGfcQoivcBT0DchB8nrFLiGb9k5HRmvl35vT/9kH/23/1z/vGf/iF/+JNX/OilcNJAEEfKqwHPwrzq2qsTNGeyJGPE1VFQ1MIZc02nXsCoVjKu7fyJSkn0IiVip1AuBbTmLCgAt5OKe9PehbYOoRYNq5EMRn1cM6MJ9egrFKBAAQMVc5jvVSEJWgBOZ/uvzL9pXYtCNwsmpDmKs/fG1VpSoEr/6pmuTnMXpoHSkXaI67nvhZhKZ7/AeN3oxX3Ceu+8VyHrAmTUWzqGfoEg7r2vc0r8vNlXaWpCtiVs5k3p7q0v0ou/Gn+ppNuYsd1XRUIDgFq0U7BJ89o4eR/YcJ919HPTnil/F++VQvc6Ovgl0cCXNxOUqAG8N+TqB8SHAgo82Qeic0TnWSSQyu8GugdEB5s/X+JZG3zRlvO/TXUbuLY9IeVAqMVrISW2XvuYYWhM+TK9ZD2cDV18wQxVBNipxshlitzlfZ+fne1+wexvVaXe3yOF4Rf/CJAtlK4b6+dQuFDGWJl+N67OPLGNs5Z7FpBQ/DuMUAh+KxcrUrcUTc1fCMqG7rUJBk0lRbf51dZGuvmtHt1bdT46hl7BSjFHNDXkRkBzk3o6x8F7c21drL9v4EnBNB7lmRpRUHyhG0zJZZwpmRnBa+QqwC+/s+fb33jKt77yBl9//lWePnnK7onj8OAhw8HDIEgKDHFlTSf8cIWsA3E9o6wEfwUEUp6RqEgwr98YFdTjGBGU7B2fra8ZVmX3YMcxvUJe/gTON7iopHUlLQsijjiviIt4ueZ0TITlBMeXyKff5+E738Q/fM6SEl5GZDqgYYebD+jpU5wmxFWtQCxZzWx9BcEFh3gPyeODgXwflWU5ozliNu6Ic9cWeqYraYnE25X5NeymgfHhDhlghyOlh8girHevSflnOBctuSCZQRwpW2nylD2SHrDbXTOIt7A4X0uRZ6LcITFCTmSN5PGMPl15/8GBd772O/zan/06//SPv8t//S//Mf/4ex/zYhkYgiOnTvNkG8BU5xQbfOVzZe9qO0fVR8CAQj2XPTBw2p+5+msBtZs+u/ta0bztb1ePLXagpEQWVMbu2j5XyFqiFu3cN2qSNmaY1ci/pfC2MyBBmgCAC+Vej3PBsjA6Txaj2QYGOmDSwgq3s3jJAAs2qEx5mwQ2Ql2eUaUKZtvV3XdxnCvt7e+t/epp8RfQ7LpI7b29ZCBbU80eWPeH69rGTBfN3HxvwFLXoe6J3leh90bIBTttNW37yzlH0tgJP6n93uS/i9mx/bYZiupwqoBUvne+0GgTopSSrt1bXp8ve31pMJCLqsk5h3eWUAjvURwxjETnWEWYvSOLmRAsG0YJMSwRAS3pTl1AMf8BzRkSTbW7MXHbVKrZMnlhmfU0JyR1tvrmL1AYZnOaEFoISLXdS19SpjC94ryiF4jv/gavP+v76tJIMSeEcrQ3dZ1FQvTL3G1K123IIrFqj6jNPfkSHFTK0RB7d39hfO3AirfylTWOuzL5nlG7TjrXuuns+8b0ywExk4wrEuU2UvVWJ/4+gt6erYova6iptAohEsHid8tTFQBIa6M7BgXU0D27zV81C9j/MkX1D1ZCt86j8/jSRx88kpTdJHzrK1f89i8+47c/+A7vv/U+1+8+xV+vBD8TnGeQkTENaLpljXdIPOPcHu8hzyu6nsn+hPM7U1kDSir4NBrxTwYYY4oMOXO4umZZb9Gbz3B3d2RW4lkg2hwHTYgq8ZzYeQfjNfMSGdThThP501c4L8juARxfgX6KHJ7ido/t/ecbvFfMEz8gQVENrEtgXT13xxekfMfw8EAIO4QJpzs8V5Azwa+mdNORMGSQxUKbpszwFGJOxBRhiaTjmZhmUp5LWYcD++CZvOKYgAjiSbunhMcf4HYe0cSSLDufZLEa7PlIXm9tzlIk57M5R56PnNYjZy88+cWn/O2v/AZffX7g64/+Af/wzz7ju68W7hZBfN5YR7GhZ7UoHjG0ue2ZYiOX7hxV7Vbd4RUgbHSkfN9h8aYR0G3vawvnre3XqIAtzGyr12HJtu6rlTVXKGLRBFu+FzsdWs5zrWPixPwDslTzXA0VpGR5dY1OaAP95ghuVVk3sNHmyNm4lI4YdBrcS7xwn/kUmlTJpd6/Tb/g73scudLdbeq7+/o3lTE1zFFBRgFIrb2edla6WOhnZgMARXsplX7VoVPNjnUdN2FIqBEdskUUVNpMyS5YNLlS2skViLR9Ys/nNg/azYiNScQcTaX2vbGIHkrUPceXur68ZmAYkRIC6MJAchbio2K1BgSH4QNPxps0YvAE1BV/gIpEy8R5s9OpZvOEZLOjbRtiK80rtbiGalNNbypyi72XC3v65QayXNP2zo1BVyfEy4k0Kb4rRNTsitB2BAo1IYpsC1fV03a/Ryg5CXrE2uLayz39Zq3qrJpQqGN4TRdfN2s1BdQDXcde6kE4MNuXM9CVnYXTNf1/E5M3sNEcULQF4rRx9w5Frob2CAYeWgIWaeVF6xavItkl3ZDW7iaQXCbPbEmJylS1M0qndO1MCPVvEUvBWgmYOF9gn7IiDDmQ3YLguRbHB29O/NUPnvObX/91vvHVD3j41gG5TiUxSMDLrtRfz0hayOcjkiK4E9lNOBFSXMjnleROiN/jwkTmruwjI+wuZZIIXhzT9IBVZ+LtZ+jtHSEkNGW8F9AFSZCcs4qBecZpIkhkmDxLngnTSIwr8voGiQmGibwk9NVP8VePCVdvIlyT48cICe+UNY/czAebv/NLXn/0mmG/oGNidRMqK48fvwk6MchInO8sl0A6ssyvCCysaySq4plBIzHDvMDyOpHv7lAiw/7AcHVk0SN+PzCFr5i2ajgwHK4ROSOnszFAN0EO5megGeYjbr5D00xOZ+J6IsUzcZ3xquynkRh25Cny67/1Lr/+lf+M3//uD/nP/8n/k7/3x3fcxYB3QtJIVmfVDnyR3mt6DtESLFRpQL7gcY7YGHLdbfW5zRxg57gyfAMAuR6PBrhd/b4esQqmv0AlrUCNrJYKwNsztb9aSIHlpstKMQM407J25jN1RnvVVUOdSZKtpe1ANTNDdV6uzKfVP6nspQGJ6sTWD6IeXGj0suU/qN9Vut0DAdlo0j0s0Caj0oyuz9u/Gn3R08at7Yuy2Y0O14d7Gm9/9haFdlUtStqySW7zV7NBqiXyyjSzdyPbudJym+yaObKQWrwoHXntXtvRyUKzpQpfWlfFtDpVkLMH7QbXhe3/RdeXDy0U8wdQCSR8yXLlSpQAqBMGb5+vzjIOVmRqtQlqf8smLWqkZvOn7Pw27hINUM0AuXrs1oNb7S6ZrQJhBQV0XOeSAxVfcu5tXRpCbFK9356tTnZ62VaZGPt+O00XTI66ONI761VHNmPQVo2wftyBirKxy/HvetofwDoSZ1K+iM23YGEmvsQfl6JR2hX8kNqHAmoAU2tKYeDFvqU1qRHSUC908a3Yeto0yOWcl3724YlVIqrz1P5boypasxsBaPy+0MRLM0x1Dq3ETNpypaIaVY1kBhyBIc9kl/DuQGLhzaeZ3/jgEb/19W/zzQ++ybM3R9IhMuuMy4lBRya3x+NxMhdgIrB6ZF6RkBBnhynPiZRvYHiNDw/wYU/G4cKIAnOcmYZrpv1Dkq7I3Qvy3Wt2EnH+xDJHhmFgzZBTBEZGP1FL22YWht2e9QRpWRimgTTPhBiR/YgMBjzm2w/JccfhwZucbs9MaeWcr1jSU8IuQ/6IT3/4MyYieopG/8fI8SYzyc/YPX7KoiPTYcQxkNcd+W7i+Nn3WeYXplJPArFktnMezpExO1yC5e4jlheO+Ciz++Y7JM44H9ApQL6Dm89QRvAHXJhxabFETutMXu5gOaHxxLLMaI5ojjgyzglLjDyIkdtjQqaZ3Tc/4Pe+9be4evSMu/R/5r/6o9egwYCb86QkeFkL4S2FkCSCTxeaYSkEvap6jW6XM5ZNqss1ha5WzcAGIhqvq+ezKvXqUWjmL0tL7eveLnu9atYVKRoBay2lDVIbmTLhQYr2T1xAnd+YNKAi5t9VfLEUaem2q6+Stbkx9nrW2rAbWmD7rpyrWnK6I0rdQ/WD2mZXa6GPOGgTU2hidTru321hEh3dzZeT3d7TgaXKcOmfqXSh0N76aL013xu/STvb5xf91bZvqkAl2TLluGLuyMUnwBVCVMckpZ+VTtWEzFWrWWXlDno1zahrtFEbraQACJWMhYwW0FF0F0ht5y+/fo6kQwHxA7jBbHslzbATz+ocZy+IBEQdIoM5E6qh1d5Ya3tRack8mvNdt5GygkaomoBc/3VmALbD1dB7DSW6jyrbVZnNptZpgLaqkpozX/nZOWi0JoBmU296wvuvqQy+b684X7b2pNvAF9zzou+mSqzb4/7Cuu5f3pB9HVhxDFIpayHuApFmVcTbM5q15BbpE7aUfrpaQCMXtZmpKF3xz5AuRtjWYGP0tTuNpElN/7utyQaCKvDZULIdiHqIcwl/LLCvbi8pEQsCtRaxitlUU3RIMJW0EkmDY4wDaYl88MDxm+894q+//x3+yi/+Ivs3FvLOkZ1wlSerSkhgwYCByzNuWfCr4OY7WG/RYQdqHveid6TTQj4C48qyewashBDI4Yow7dmPO3I64+KReHvE6yPOQ0LmiOYRhhtSPBGSMHo1RjoJKYrF2uvCfheY71by8Y7hMJEXIc4RmQJhPxCSkl5+yBKuCPsnzC8dR/8UF47k1z8ifvrCkgnlFc0r4cnK8eUNw7znkz96yZu/vMcdEikuTCEQxoBePWN68pzTD/8Ntx/+KchIdgFYkfMte43EPJOWE0PykBaOH8/Ed8G/9ZhhOZGjoOkGwcPkEVlwMULK5POKLjOkhRjnZjpy3qFuIGdFCeyXkSVYsSc9JtbplvXRNd/667/Gf7r8jJ+9/L/z3/40MXjIMZkDcrY0z1ZB0o5FjvkSu6OdZrJsy0471qTLKo2p6Upz1VIqLWrO6aZwEzANSNVyFXCssUYvSJGFSu2KEh9uvg40bTeFemz9K46CTcQpkrpzZC+Fl26aNRUz69o56hms0YnGz7/oagUQoPpGQZV/eme2e7TwUnxn02pCK9hU/0m4ZNItwqGuidIEr8/RwfsgZMtMu0Ucbf1opsf+XfWJJqgYfdkK4tV7N18rioAkKtSskjbfxb+qdV0bp6/1Cer/qiZZBUtZLZf8SfvprzRWNouG3arglZix7KtljXM/xr/k+jmiCSxcUIvDYPSexTtERmYH0Qu40Yin+A05Oim/GyLcbPI9CKjQukyo9syf8hkXCyJ10TpAcAEWOwb+uUx5fH4rtQOCp9nRWsxIfaDbhH3u/S9oq/2QhoA2BCzFdICAuksGXzUlTiHlbZytE1VCl23jtcNk6nCbZ5t78dA0AMXWVDe6oiVyaCOCLaSwqLha34QtDWY7W7aRN+3c56V6M89tjL5M3vb+foIvbrm3g4smw8ZX+lqzIha1Y1PglA5qOT2iGZVAJjGoIy0Dq0t8fUr8r37j1/mV332PNz7YMT0/o/6aJJlpvCZwIBPAe8vEl5S8LmQ/kEIi+QUfj7j1Fkcmp5l1npE141Zhna3SpneOvO4YHz9AJ7iNL4jnM9NwhXvzAyZ/RdKVHN9BlhPMH+HmPZpeoTniB0cQXwqOKDmtOJfYTY51WUkSkDCwHk/s9UCKCb8bId0yf/ohPPmA5L5CCK84/ez7xLvXuOWOUW7JbiEcJogTomcOhx35ODJ/8hmHZ48IbkSnzJIioTjAPnrvW4zTE1786I/xLLi8ojmSc8Sr4L1FJmS/souB+aef4J5/ANHj5TVwhmFCU8KtMzlH0hLRJZHjYo6LDpzzBO9MgyIgDKCj0Ze7M8EJqoHxdEKvjshh4m/+lb/Fd7/7fb7/6d/ntF7hVUi1BLqaybFGBFjp6lT2bW6+SlrOoamKMy2eu1Pt0/G05hiotDBAVatFLyXSwGmV9m3P57LfL8o0K8Ygi5YOzVtWwfJSO/beTH/FIXKLlinnpNCvCgQyWjS71V+r0rV7Z649v51JY1aVfpdB12cKAK/a3Uar2j2ds3Njxv3LCi2ogKgz923PyeUzHXCz/ne6/AuB6vKzCjI6ayito1J/qx0v4KyCnAuEopfv0e4RV7SwdDynvLNPt073bm0tV1pb2mzPb3S0vboCDRGytFQ55q5Z9kKfak86oPMXXV9eM+BHCAPRWdTA7IRz8ERMHSXOKhVmqlmgIiUu1rd6pdMz8roI1U6eM1sq4c020rXyBcy8u7YZvvioMcuLpqo5oDL/Hqn2fdvebWoux8VGbWOpKuveb6BsdOl+p4QRNc/+DdQgqUgD20B6O7yNZ2PSUr2DcwLnccWByhWCVB+rfgWIJ1ezQCMKvTPmBhZMdbflut4qP1sIlFSNQnlLRbu1WanvuJgr67hIh7ibGrGMlz76YPvZSmmL4JxHXMPXONnsZhnBFc91dZEprjgfWHLiEDJ/89E1f/e3fpXf+B+8z2n6KcebnyLzjxiefA139Yh4PkPYE/0BdMJJJvg9TvYwXhP275CuPiCfXpKOH3G++wFDjIzqWCI4dog68vGGHPbotCdo5LOPPmZ/pSy3L4lhzzgdSfkdJj/CfoLdDu8e4R+tcPspy4sP8ctrPAtDKBWNZWTNmWkIxDWjMwRRcJnleMSHQMwLYfeMUwRZJ4bhzPHDH6CvbhjliOrMKoJzE4PsjeEER5I7Hj55wO3Nmd3+inUPbglF05vJ4cCyc4xvPeLZ9Evc/tl/i8SFNZXCQqNHVkiaULdjcqC3K+nDj4hX73DQl3g3IJKQdUHWiRRnclyhOtCJEkIgeEgaDcT6gEZPjuZojERSioTgWVLExxOka67eecJ//3f+Jv/Vv/4n/NM/E8uwmAMiZ6x0eT1Audh1Nt8jK5ZUmDSKy7LRKSnmPt0AcwXExper1qweI1NNVwYpIqbpyNU/ZqN9Wp5Hi2qX2GjQxvu0JHZz5aTJFjXTJSjbTOsV+JczJdvnFM0aF8+7Qo8ajKeJtY2X1MYrOOjIHuaRYEe6cteOgzVufEkDWuP95z1b+BwgoH0pUtelJx168YzC5ud0wTcuBZTWndrXPnihqpKqJrvOUS50qpDMVBYgtDItxS9AhOowuEWPbazxczChN1V0/d0i8Mr3bV2Ls3LR4tbYE2kp7r9o/j5//RyhhQMZyzWQgwGCtVW5KsNyrtirpcR7+zY4LYxEVU3ib4585QUZqu1fahW+xpDKbDdmWJmzMU6Lva2MrEibjfna5G2OaD0yk8Ic7X5p9m7p9qNcbPjaHiLUeDUjABUAdMi6ttvqhXtUXFu0htZrDHFBfVXircNuawBo2YwtakerRsPGq87st6q2ui0bYclOlnHFhuiKCknKkIsCUUs1LWooVNMVkHNxktFKwKx98yuQIolsnrdmTtiIWqtcVsZUbWAt06TTy21bh6UlukCk0D1XQmg6N8KqjVJH9W1QSUQRvNq+nZ3nKz7xv/m9X+M/+Q9+D//ewkcvv8vu5ZF0s3Bkxb/4Q8ZHD9hdPSVfPUf2D/ASyHiynEhZyG6HDw+RkvzHP36bcPce+ZMfkG9+zDDMlichLzDv2B3eYXp6RZKX+OUFy+0tPinnu1uWH7/k6s0XrONDhsNDwrgjDlcwjcjuGftnb5BefMxy8xM4fYqXFZ/UnJgQxv2OeIzoacHvB2JecCvkKJz9gH/jO6zxyPEn3yXc3DC4mUEy6xxwfoBxQcYVTQM+R/CeYVC8zrgQcVnxayYtSnYz46Hk9wgj08Mrpq/9Gp99/w8Z4g8R78luBBmQcDSGKitORvjkI8L1I5zuQW/Js4PkQReYYzFyKWF0hCmAg8yCkwDZQufMKTmy6swQHLI8JOc70AWNr/HxIefwml/5xV/lb3/7t/mTn/0B8+qYxju89yT1nNfMmjIRS8usudIhY+Q+u+aIZjS8kmTd6IlW3RbNyQ90kwrriVGwRELGSNqxzdXJVi9weAXuWpio9LSEyshrFyq9ce27zeTQA4vKO6Rp6VqLDRDYYHITPgotdbpl9mz9qg1W4WE7z/UPKUCghSD2PK1JvZ1kU7lix4AdRRNZ+96bb1pTFZTY/LZ1QpGajbI8vwl5PbgQLl5e6Xrh9NrGY/PetARS+ELtfjEl2DYwZ80KyjyUaIzyPlUyJfNn8RhoeK1GNFTQ0i+U1DmovlGu5Imxtc8ISSFIrURpPmKi1UT9l19fGgysPlixFh+I4i2HgK8x+BZm2MzsiAnOFAbQiucUptDQuZTBlERCWhhILTNatAQt01KztxcGRGy/SzWgOGnM5SKJjpTIANdtgroxGzquk15XwdAuvjKzXr2mhfnQJN+qCaj577cttzFsaUyzHPhWKnhDmNrnSSjpRzXHoo6TMkeW4a2ds+LrsPnc1Gp+JZ+B22yF1ZloAyD2zlTmzaITYvna0xz+MMZs9q1CcMSBxu2cUYhWVoov4j1pRZozqBbAIOXBauOU3E5cObeV4JmHrlOPdxB9FcOsUEzIyhASu53j2UPPo8kz+REXApJX5ij8j7/26/zd//T3WJ871k9/yoNj5JQTnsCYM8yw/uxjVv8Ju0efMD56g7Tf4YYD6nfgJoh3SFwIw3VxUMu43UPCu79IunmAvvgB+fYVwo6H73yV4fkj1vQR68tPya9u8bOS3IjOd+SYuZ0/4vD0zHq8QaYDMg3I4YDsHhJFGJ48IRx2pJfXxFcfk+ORMShrXgiaTeW+zuTzyuBHJATgCfH5B+ThNcsPvsduvsXxmtEpKS/4IeFcLmUzAi44sks4F/E6sxsi8dOX+OdPCCkTsHvnm5XxkMhpgunAcP2A6298h+OfnND1jpGJ6BPiB4JaulxxguYVPX6Cvvcr8NlMyGdiMqcxJ5nkrOrpOAS8VyLRCJ6MRtRSJifLnBma/0i0vZSUPC8M4wy6wz+e+Rt/9Xf5yes/Y5ge8u6jR/gxsKTEi5cv+OEnn/InH73kJy/hLgI+lfBDhRxxXopfk9JMWlXVjzFz254b+zFmL03rKeq6mgIbcHBa4p60VK5rFAKoCY+0+hJU57J6Ruu5cC1fRJ8W2dJrW9+tlE2VV1yRWgFfaFrNhVLomdR8L02Iso5bCufEZu6sTHkz/W6cy2/0qDjSXQpwG7PbCGSPFGjmwQaUmhB4Dwl0jPLSmbiCoi1CpIETQz/9wxtjb1qfMs8NO+im+XRlXJlmGqEBrArOtmF6bC2yKr76WdlsQ3PULvkkCgA1E5bxT6RCEqN/yRnDHgoQGQQQR1KxODqB6MTM+eIYCghVd2+O/5zry4OBEFDxrM5z9J7VVSCwMdENgJUFasmAyhT3Nvbu92oOcFocfIqjoGkRAFlpmgSwDVodCXNu6F3qYaRUq6shFSJsGoCNA0qr0y1FYm+7owGVTQQojLB2O/dos0OPVYAozNYwkG1Cqf1qzjple/Q5Dvo8A2WetMYjV6CSKxMUAyQplX4U6dqHAhgSBNNEbEVbyjOUdpoWogNZYiCgl+QlgzhnVdAqc/eYate5zdlTa6nrin77denmsmwWe9YIVCWqiGz7iG4/iVjBH2cSrCRHVuF6r7z1AN57PPHOk6e89/gNvv7Gezx/+pz9fm840C3kVfn2+2+wPDijNyfCkplXwaeAOgOWoh50T1pXTh+/ZH595OrRE9xhR95PyHSFyxMiKymtpDCZ01hKpKDIo7fx0wPWT/6Uq/CY8c1nxLuP4OYFaV3IdyN5PREmxetgiWDWzN1PP+Xw+MB8WNAjjOeBcH3EDXtm5wm7A+75mwzjFenjT1lPH+FkIUvGu8Tswa0LmgLuSsnPv8q4f8iLf/kvOMhCYMW53DIhZhdxQcCZT4RXmJ0yaEbzwrj3rJ++tKREjx6hcyKvVgUz5UzeR4aciJMwPXqM++AXufvTP8I5C5sUUkk2pJaKGMf8s0/R98FxwKdborpy74D34AfB+VRK8SrIDpXR7tEEay57Z8BMXRZS61KCWXHDGdlfseTId375m/wvw/+caXzAg+srvPOc1XN3e+TDn/wJ//pH3+Of/eCP+Kff/ZCf3ZgfildHcispu+bVYozSNaevSk5agS42nqdAzmZW0+YIyAZ82dg+VKfCjgFVUtWr1avQUL9szGwTWCjMQUv4sDkLG93RRpcLM6yRTfTO04XR18Hcl56bzbnSvHt2emegrpNMOsBQ+thLBbXb5d46nxtw2JrpJKqtT9rfWOdte7ACrApqtrvrKvWAYHMFbE1fjLXOx2W/pMAtzduzLTdKmfNUwJ0UTWz1KLW3l1LUgoV9lkqEiCs5YUoC7YYbqtO0laKzfltL2RW+IkISx1p4n5mSSonqL3F9aTBwFiF7z+wDi5h/gBTfAAXzYvBsNuiqDcifX+ON4dbc+7kwvbWoqM1UsOXfTsaNKCq8Ek4oWuM9y31lQ7RqiuI3TUAHWKgotjdx0H1XkWj1/mzflauuv3bttvwJ1cGvaCbapiyMvDG22lB/8C4a79rr5If2tW30lm2sefJqGVtuWoDmYaJgtQfK5ihI1wBJPSSVqUth8DQAU3MkaarrBb7SAdGN4KTN36CtuHR9qBJRlbDcNucmRfZr6tqSJJ/Z60hCiUl4Y5f4hTev+c67b/BL73ydb37l6zx/9pQnzx6xf7pDxowGxeeMF+U2rzAkZL3Br2eGrHjnEfUsrFbhLZ9AAkF2DGKZA48ffcZuf417sKD7W8I4ou6A6A1BHuBkQKMnZVAXERz7x+/ipwnCwvryNXoE9coUFHQEYJxGPAmiRzOcb27YZcGNE/NdJL1cGZ9OjMNEvL3BXe8J1xNTeJv8KcRPf4ziyDozSGBZATnhzoHsDujsuJoy43LG+8zgYM0RkYgPGe+tXoKEwTQdHsYM5/nI7uqaMEE+3cHVDh8cHBd8EE4vP+U6PEeXhbRE/NVTwrM3Obx4xfzqE4ZhBtXCVB2ymr10mCO7NbJMj5jnHxHctZXbxXJCiCYDks7MZ8Fj4Ya5ONKWHBmqEeccbih+xqzoKuR4JKQ9mg7Ioz3vfesNcloIDnTIBDfygD1vvf+rfOubv8y3/83XeO/pP+Af/dG/4V/9KHNyCUli2rTmHCMbuap7sge2VAbSqYnrMaQasppUsO39/sRXOlffWFXCDTrU/7pqmSzflbjyyhQqU3WVARlz0UaDygFuyeDsM/M5qmAgX2pPL5huZYrCpiGo91apqfwt29xt+vQCrnSby9bI55nEPZp97/M6J1Lp/v1Zle1e7dl9vryl8fvqz9X1u16OLvSQ4rNS6KVsgpqZRsrnTpHOYdS2SDEJY86pZm5VXC50DldMw0XwqRpmKXoHMVNOLIBOCj9RLPopi5BKhUoRz6rmx6NyfwK/+Po5MhAGjgKrFNud1g6XTeTdVoRC2cAANHTcr0KLsGx5AopXagEIzVegrUCHMlusqW4mnrpx6FJs1kiGe0zZ/vbb72XDfA4UlPfVA4qw7aXK1Out9Xetv5cDV9E40LIt9s3DBdNsQ+xeVpmsef1TnIg21N80H9TXCSK5Y/DY5i1rVb3tK7jQPlSocl4t/S1zoVlJ2YAGzkwVlIIvmh218E1NiqHlgNUqXNJ53Wh5d+eYXUAKDRAYgr60mXr1RLcSRPmFZzv+1rfe5q9949f49te/ypO3nhIeT8ioJEmc0msGceSsDFl4pQuDFwZWYjwi6Wzj0UDwe9CFlBb86FmjvdOH0STVpJxOr/DLxHi4Iu4jcTzhTi8Zxj1h94QoO2QZIJu/wG73BOWO+dUrlmVmCo7hDGlMDGFAfLYQ3byS5AwqTIxwd8avGb/fsywz8cPX6KM9w3SA+Jo0eWS8Rh4/R88Rd/MCDQ6fM6sMiCxoApciy4vX7NzCMBiDyHHBSUJJuEGooodotjnPpmEagAll3gs78cAKfiQ4TI0uyvn1Z+wePCKfbog5MeyeM73/VZZ1xqU7JCZiYT4iCs5U8fOnPyS8/VvElx7xGY+34lcpGuh3AdyEk2CA1nk0GTgN3lshtFzoDx5hBJSUxcIH04zTGfXXqEzE008tv0Y6gFPUXyHjjodvC791/Ts8unqD9x78I/7e8/+OP/jj1/zstOLyWkhCIWbVo5vNktyf1SrUtnNbydq9cMKKs0WpZQk6jV89dFAy2rOp1Ss1MBpVz099vxMLScsZJBgNk6KirAHJlx2uLRZmUgWTnp607y+Bz8V9bbwVKBRmX9ut76pEutBlTfmizQuNbB2tK/S6al8+x8+q+YaOXv95gKCfS7n3daGLnSakaSrKiwWxvVNf1jSp2/pQ/KNUzURrIdC+UmqUbKZZqra0849SqAGsSQFnhc1cjSEpNN+xaXwyzkxCVE1QMU9nmxsTEot2Id+fly++vjQYiH4kOYqDoDObtIiBAKEw3XIKKqhuC6mX6hfNhXmUZEHFTHCxnBf7t2NoWkwDFJ+CtpBV1dVFB1QHvWYH637iLjd+x+ibN2fbEJ2vZ0GAbQMKUNSVdjJLP2o4YXtPfVf3s8yH1HTMF+PeNmd7R3FGaqaFIh1otZ1XFaBQVE9ic1eNlS32rhCdyoCrdgYtyFURX8EZNufOI2pOhO1AFEIn3qHZm63TcQ+obZKSlLmtKTRd5x2ppV6EFieaCgQqQADAKQPKd97Y8R/+8nf427/9m7zzta8wPplJfmZJC37RWvzSDqgmknqGaWI3eDTOhFlx2ZFSsZFPNr/O7/EoTjI5mSlKnOK94kdhPp6ZXyXGeEU4eHAL8XyLnm9xux3OPyTlK9w04Q/KcneLfnbDHiWxgFdGv0dkYZgcOUNShxszeVWCwDoJcT0yLgtj2JGjcPrkRHoIbhwZk5Djgt89ZHrzfdYYcfMtYMmKNKul3s2RKzJ+PUMApwurriYlq0WGaLZaBA4DjkFAUypOsRnnIEpi7yBKxk2OdJrxKOtyZok7RkZgRtczTNdMb7zJ/NOPCSoEP+A14bKQJRHGgfOHP4E3fwVxV/i04qcBooUXinOIH8GZ+cByezqylCqlWXDZImVyNtNBK+er4OKC6kjWiOhAcAeWlDjNZ8J6srH6I4QDKTjYveKDb49c7X8X9teQ/oB/+kcf83Je7Tz3woxQyHX1SaIxcW1hPtvZ2hhd6V/W4mwoF6StpzNNs4Zy3wtcaw6VeiYK+3DlXVKqdGawVLhuc6StAo9WBuvYaFRvHuhSln+utkqldypU4N8IdmOIBXo0plnpqrbpafdf4KxLm//nL0fHm7e52sizvfcLNQRsDzbQcB86XD5zz425uT80s0EjzdJ4xrYDik8WFslS2VvGSqrX3CeWQ6MKYRW4mWbBVthfzouUmh5sjqBbzog6AjGep2VPdQ6GX+b60mBgFitKJEXtZM4mVQ2vG/qqDl1iG1iauqv3GVAuMg8WeK3F/i/lcLcnG6ArfgJSnOoon0mwDexKUo0SPmefF5RZ/Qc6lTR1AhsjvQQBG2Iv9zXmCRcL1YBI3TVuAzCAc665IGw7uA79Erdf2MPKy9r4C7rWlDZwUdR+UrUipTvtPBa/CPVqc9CBATuopeHC7FVDAWYL1f5PNtBBzeRFQcsCmrQUAyoHVhPa1rdUtxQ7FFJKUKumgpyrpoI23/182NKVe8QYwVceDfz733qHv/4bv8rb33yM7k8suqCLYipz0GR224T1J/mJafeA6FaG5YxbTuiSGJwdQPFCCI6Es5SgGeKa0FzDZFeceqa9J82J+fXH6DwxXl8RJUCekWUmuoQcduz3j9H1Fn86s2bF+wEXZ3LIhMXjxgFHZgjBynrnI2gg5cxucCQXSAuwJMIkTH7i/NkL8DvS9VPk6RlJLxmv3ya+9VX4/n+LU198Ohzee4IM6PzCQjER/HLGOWOk3pX8DDUxWI7gsxGvlIliEsioMKfVznFckeAIwUO05DhhXpHREaMir46Mj/eMjx6xfPoI4owTg3iiECNIXJB54rPv/guev/kGEu/w60LSZOYaX5KVOci6ILpDOaCDJSiCPVkVp4uldZ5PpPOJnGZL8ZwFjTDoGacRP+44rQucT2Tn8INHh4WUXpNOJUPhEJje3fGb7pt8enfLq7sj/+pPXnGTcndWpDha6sbk77EMKb5R2SpgVYrX/H200BwnJZiqnGXzHtuU/4VIdMxKGw9uMkh9p5NGxlxhNjXHACW6CTqzZNHaNcbc2u+AhxSn4EboSlRFAUEbKKgEpm/rnm261YDY5m1DAPWs91y+b6z7vNDb+mmZ2Q5w1XY7QtnIqG7v7NrSCyThL60cXwAotNeQtHWroKaahsGitiAV4db1ZBPTsJq5OCBiPjjmC2AqfmPxxQEVIZU1VTxJsTLUFHoLRpfFtG8G0XyZH2lDd2743Hi+6PrymgGptqXCXF0ue65I4n0BjVres6K3njFprSeuXc6BjFRnDGFTf+fKmCv82sCEBQ90dnNGNA/Fp1EKc+u8Wz1sToH9xivSclFFdlya6r1PrQKFoF4Kyi8HXqTZ3tq+k1pnwOaqhewUFXgzgdTDiW4qROm0BPVcFKnCEHJNJ2FjsS6bdkBUkFQYeCl+oZ2TZLua+k1tPTs3ATswzhK0lHdJ8UloHgTZt+5bfxPqjMEbcDApRFC8GAjMBRBlrVnfChLWYg4o5NM5q0nvkydJIuBRiSQGnl0pf+0b7/Pb3/l1HrwXOecf4e6M2UqySI2cV1YskYxXEHlIenxljGm5QU6vkaiWVjYnAjZvafC4tUS0ODOFjaMjpWgakORxYcWLx8me5bTAyzPj7kA8CzpGUlQePnmAC0q8PaLZMwSPT5GIEqIVjBKXcWrgyTnIySPLjA8eFxxr1SrljC4rOWR2w0RaEudXrzjsdhZx4XdMD5+xvvE+8aPvYb4IA2teGeKZ7E7GEFLExRPL9Q5itKMQS4ZPn8lZGNQTJZn2Ys24mPBB8HcRubtBvOCniRQAzQSUtB4ZRo9kR5yPDPMBponp8Vucb28YfGJxT5GH1wxXD4lXA8Nw4IqR9TCy6ol1PZq5Jq0MLuJcRGRE3IHsn+P3e4K3AmnROZKTkg7b4zWR54V8PJFOrw2YJiFHBWYrqsZEjDeoh0xGF0/MC+oWlIAbAkN4wP6J8O/98reZcuLq6r/hD/7VHZ8tdqR9FnJKhXS4Uk+BkkRIGAiWsVON0Es5syglwqDypaohNUbo2jHfEgRtckqlPQV0a5Eqm9mgJJ8aXKHFVaixyJ4klBC2EjnE5n9gtDkibrj0ayrcy8znVSDypbhWMQPXmg1N4qBD85XAaEer2fpWNQQubbS2CjRZN1p40Zhc/GV4rDyHNh5PA2J12jpQACB+E3wakNPy/7yZJbohbD97vrBNFyUi3HyyHDkWVz1JluyqsGXvhF2CWZcCtFzRzFnopi/TqyQ8DqdbmmJfhNtceE11KISEIxTmXwXT6nRY51bBO7L0KYj+/OtLg4GNSRcwECy8paGsqgFQ2dTL2KLYQZDa1YbYtJvxDamxMS/RTpCtoKAguXJYrE/mXUyty+1KpcQqCbuuL+IaSmxApYfcbfELWu9VYuWbLQd/DfExZL1pMWSLNijvk25c0tAx2/y15Wy7m4Z2pSDTdviqSUaLBJDLMCxEqHltum7KXO24dHu7SCA12Urb3ViSFykMyQxPZS5Nc1APUqbkE69ItMGgOh7p/CyLTc1VLYJsRKebYI9pngZv90cCT3zit9+94rd++Rd591tvkPevOM6vCMcTQWc8AUuHLXZAxJNQ5OHA4WqPyw5yInPCxcyoWtI7BFSF4APZq2kDNOEHI4reTbgEqzuD86gkU6cTWI4z8Xxk9AfWOIBL+P2IrBFKYZ3BGUP3Rc1rlcCtUqKTItkFT3QGPgVl8N7U5EtG/GCmgOCRycEaya8TjpVbeUVwsI7C8XDFst6YttdPcPojQjgQryZ8GhlHSLoyJgNjUc15cBCHLKZCDyX//mldIGWc92iOBISYhJzE8hCs4FTJzswwPkc0O5b5TJj26NUTwjd+BdlP+P0OHSby4AmspLwS8oJfTqQ1sVxdIWRcFtZ1Jc4zEj37/QOGvZD1NTkr65rxOhRGV8rz+oAPA8ODh+j1QxJCnI+w3hLOC8iAjA9Y1x/iV8+iK8hgwFUSYTCGnPItOx346ptv8PjX/iZv7p8zyf+Xv/ffvOY2eZQVJ4Gcq+kvWQ0PtICAGnpbWEwVCnqS0q5ynzbZoQEFqgmtugg2xi8Wc9+KnW2M8qLtSnukVrwrIEI72tEYaWcGKWY7nCt0asu1YFrHmqit14f0wsU2kMa6ZRP0LmgOlPG5FsHYxtKZDbsBfW4OexOGVtrY3qGXz/eApdNsFFm+UwYU+tfMu/0wdWtL2L73Xce0Tqk5DlZ50xcnwa30gSI5I2IgQdscg8tVKPJNoyve1tLCxLsARrHid6mbb2oOm9pPVzXB9+f1i6+fo2phjRwQaqIKrYkaOvUZsIHa+yCv7vwWi1OYXb3P9TduzF9ctV9vDVo635JQQYbyr5oFgg1NQmP8BXo1Xi8Xq3i/k2zjQi7G1oB9M16VT/Vei8X+Ji1HOJeN1NdUpnj/3WWyLtey7xvG3DXT0obVuOBiK7pffMPMHNt4tPhytLSbdTyqKKH0Pzc1PxRw5hQpyVk0WfuiBpxEqg7UCI65McjW5wr0sPf2ppjqjJqzIuatRhbPJMp3nk38+7/wS/zqt9/h4ZNb8GdkFTRDOi/EaGYNweFcIHjbE+PjA84H3LqQl0ReF4trJ6KScePOEje6gPNCygvqHQmxUt0pol7xfjT46hd8sLBAlx3LaSWfI+EgyLAn7Paku5e4mHAkvGSWmBiCCRBBLDTToYzekchAQHcTOaVCIBPBC6vLHO9OPLq6ZibjgjKJMs+erI6oC3cvf8YaX7HImeV8grPiB0BucNMDxscT43jF6h4zhQPrdIO7/YSw1gqf4GRE1xNWjMdBjOR1xTnHmDN5XZFpj0OshoAoPjjifCb7kTUofmf5DZZ4xl05xt1jRIVVVzKv4bQyzyfy6YjOR16tJ7J4RhnIgwFEJ+CDkNTz6pNXXL+GB2FA1UKm8MHCDp1Dg6BhB26wminDFRIGwjThRk86R3I+mk/DHMEFvAuloviABLPdunUg5Whmlt0r3v36E66m3+GoN3z68h/wB39ytAqtEcQtZkZrQNfObjMD9Ee8MzV+/spNcKhe4pUc5mxZPaVKduVQSmGWNbtfLoxS0E2qbrH0G7X4HHsVqKbU6spW6bHRhu1s9iJKFeqk0tEGXtj+LlRVNp5LTYrUOqNqdLlr2+ZCaSbIC/Ns6XR9to6oaqKr1qECIep9vVDV0fB2D7R8ApcLt73jot0OEFy00wGtMmhj2toCUhSL/1etvlC2VtKESuNP3lHo6OYcXk0FCiV9do1Ucy20lvt7rPl+VND37xgM1BKZiBhVE2jeEdtMljUojLFssgtG0204+6s4jTmQ3HmgFv8Apdcy1P9UFGnSoMpozN4VG29Bng0E2AAKf699cca4KnLuwtm6ztl/NrG+bNzSfjfsNvl184jNWQvro76DDgDd8xdoJyv3DUNlsE3LIVtugWZMFCCBL/bgXGb3whGp/N2m35zkNnDeq/4MVWpxokMwe5d0GhnEUG02dWjxPCuEQ9p9DYWLJTXaJB/awa+YSBWyGxglkQQkOd6/En7nG+/x1/57v8LzN5QlnfDJzBXRgfcTOWViTKiuOLeS0hnv9+xLzgXSD5F0xi0J1isYFpxEBjewnBMpBcQf0CmDHwxMZEVyIqYFWU+Qc0HsC24QJoHB7bl7cYe+OrO/+ioZJZIYEgw+4CWaFsCDuAHSjKRIWqOZTJyFuLpxz+gS67IiOZPzwm6/Q7MSndVbTMkRQ4BHT9GHasWOTi9ZXtxx+1oZ44BHSceZ0UGON9y8OBEOZ4a9Y/fwAfvrJ8j0HuHmNbvjS0LOnFwsef5tnUNwSE54VWRdLd4wRXBa0kErKh7vBlaZmJ69Q/YDLjhUI3E5Mi8JjifSeuZ0viPNC3mO5CVZSV1NeAev4gJzJqbMmgU37WDI7K8HboMj7R4wMhAQFjJu2OO85WWWGPFDwGUr5ZwdSBiJATR4cnKEw1d4/MbKzes/xoqsmhAh7FBmMoKPA5mMV1j9kcdff8bvpv+AH3/6Y3764rv88EU0dT8JwTWGLO0Eb7/dpyLV2bD97GhLrf9h9GwDD1o0n/I5zVlFDZ1QUs6NtS2FMXc8zXU+Az3B6mlqJ2xYWXlK2txKf+yGxrfaZ5Wdd0Jdofe9N/6FECBsdNBhOUYECxMvTLGObbuqk2N5W89z2s3amarrR18Eh/qPZPu9+Td0399jB70g05xIs6DqzCwrpQS3WOr45ugnalbo7PHii7+IUgNPa94AE/xsLtWZJiCBmXkKTzWgYQ7bl/yteKk0flPoMYUvfonry5cwdg4JRX3UJrCiPm3rcbGQ9xls2z/d4lYUVzdfvwAVGbfyXxWdVcbjyhAcW4Eh3zFHt71Dc0FRWz8vPGbbQveDqBL3xvgv76+brftXQUBj3JW5Sjfe7n16b35EimYlF7XgNictykGKjSt1AKFmS9MSWyxs84XQIgn6s1GBQtcHwZKlmCPM2sYsUjU0arpuC/DGssSZdcyeNt1ZDcU0yafmXICagKVNr5YDU+dXbEOvCk48Bxf51bef8Zvf/hbvvveANbzAp4CsCdHENE2kpKQ8I3g0lYCcMnfKHRpvOd59zBg9YzqDA58nkrtiGR/D4REcBJ1GPAO++rCQcOLYrRnNjpRXcjySlzvS8YZ4fo3IDdPDgVc/OiGnE4/FardrXArSV7zWFCUCOSLLgqyJuER8GMg+EHZ7JClBxOLp00LWyLgfmK3YAck9wD19k/XqRJxvON28JL88k2+FKxWciyRWpuDILhBGi5tfzwspB47xxHrMTE+v2b/5kNPNxPLqNVN8Uc6GWSqDd9bPnMypcJkJ3mzPMo5oVJILuAePODx9xuodkhbi6UyKZ+L5xPl4JB2P6HkhrZm0ZtQni0gC8moM1ok5TA4uMIyAWwnjhGNE9cytrFztH+IWzxBX8npivTuRyZtVvTkfOnADLgT85HD7A/7qGVfTO4Tpmpub70H+jCCBrKGQEoW02jqtGZkXcDPvvvecX/v67/Cvf/QzfvbyBREhxwkpzstZ+wijeswrHZTOLWdjSJe8pQD2agqoZsvCZM33QDch11VQYPu6eEvxRVelbRWUf/6+Sge061Xxecj5cySt0QipZ7QO2k7Y5iNFowt2UxcyfWGi3ACUhT8Xp7pmBun62XezCWV5wyJqTHgTaqr6P3f3t1nZfpd772h/dmvq7/Wl8Tnd5qXxrDofZjaW8g4pPCxpRsXhRcw8kDMRk/RTBYJlxsSbQ6BKjQgx2q4IiZqGuBPcLgTR2pLb5rOTW/+i68trBrZ4Lfun9jKppqemUqHLMXAfWtVJ7Rcot4nbbq8nwNrdvmq7ojDBKunWf9VsUFUndWGrN7yUTdPD4mJYr450fT+7Hxeft4NEY3pbdq+CxF1lftLtw4qgddt0jo2pVz+AUoLXVIN1XussdAeqEB5ESyx12TT1NQ4zFVRA02qQdwe+fqcVfFQ1XaSBINWywV0bH+TW1Yp/cv3uPuGoz1cbWfUFEWdhcE2TYKN2msluwCk83i186/03+eCrX0WuExAIeUbXVJQigoTRYnq9mmNcyVXhRmMMKUXWuOKjouvCzDXh6i38gyvydE0YDmRZyFlx0TKDuawcX70ieAjBW6imTwy7PZKvWXcPcOfH6OmI5E/ZP3akGQIOyZGI2dU1LWXplJwTwQmiHqdKOs/4EZCI5kxyNqfeewYfUM04H9jrxNFfoW8+YXUzy82nnG9vybMypYAOM7ra+njvGVTJEolu5eB3RM0s2cou57hyPq249waGZ4+R4Q3iJxF/emmEqm4pB0hmcEouuQkkCURF1cPDZ4TDNSqZuLyG+Ui+veV8dyTdRXJKJKeIJryohfvFxCAO8zM1Z1zxipdodS+ykAloVNStzCdh5xb8gzNyeMzySWL5+DXEWyQMEISsHpEAIQITEixN9YrimVmuPsE9eZPw6MCOZ5xfnxGJZeObs1ZSscRRBHycIUbcIfL1936Rr7/7Fn/wJ5/y4pjKkVU6Dm2MsNvvNOJdzz90RrhGxUSkOSE3aZrK4Aug70IIRGrEQdHM9erp7teNf6qBki+iYWUc4op6vtK/nu5KNV/UA769e6PhqY2nG2yhzcWFvvqSUWh1JU73sAIikPxG88r89sLQRvprO7rRmNaRewy8N0PL5166PXwhKOnlPX0fGwMuK9YEc4VaDyF71JV8OeV+54SIklUsUVs1hYjxJNO22LhyiXiTQoul7IXqwJ9wRZDqAIBoF8AmCKEzZX8RH/789XOYCaA5ACrYBqrZre7NYT9ppTNS1f5dkiE0tiNwueIKutme21FSWvwsNbSxoiJXQgmB5r1R1NINrfWoiYqCyyRXD/7PjUftXbnrW9NsGPCoDoSV2Tb1X6dK+8JNVUFTO4cd4xY12eeeMdKiCaTdg9OaGoBW9BpKoqsyNz1KrIdMyhp2mdbMHlrnoUvfXLQOWjQvWgtNlcqFONMIWPez7apu2BlBvSvAriRZqee8rF2dalHIfkXdgEb4ymPHV5+/xfWzPWlYkCVDLHODxZqLy0gW/OAtE1c25B2mK3b7h6S8cBhM7R/1ivD863AYiAI53qLLDcvtHYFAlgV1zrx4lxORSAwQ58iAEpPihhGmkTAJKgfEjTx0E3dLsj2tZn5xzhE1W9pfsoXEiSMieC2pZVzRGSwLQYUUF+bdRBhHA1YpEXfPSE935LCy3h05vT4jaomBvDOnp0zGB9Bs6YCdOM4pg5zZDRPeeeY8412AOHHzg5c8fA/kwTPyu+8QfuiZjj81y5Z3pKh4IjknC1tzHs8McUd+8oR8dUDW1yyvb1nSynr3knS3otnhUYYJfErEcyblSBgigdGIWVZqhtG8WpbI0Q2mVZJg0Sp3dk/8bCZNr5jfumJ68wqnb7F8rIjM4IKZXryFFIfgzOEyJ5P08Sxn5fjx93Fxwk175HBFPM4EiUTR4rWteDKShLwEnF9Zp5m3nj3hG29+g+dP/oRP787gLS+ElwHyGZVEzI6gph1LCC2aoJj6nEoDV5XH+cJ4snSq8WYnzxXFU+Pmm09Y74gsG72xiAFrT7pkaiqpZC6vWtNKunUr91IbrOo6zdT8LNrnJ6m25/r+Ajagoyu9oEYGZ5rFRsaq03XtSc022sLM2fiJxdtt76zCV7nLZCBppl+pJkq0/O07wNRpsN3WSgMACk0Qq2PozMn38YVdRTcjNFpW6beU762KrzkLimqJEoDoKCZxUHGFl5foOmf3ePFlRp2ZITC/GsWR6HxU2n7YzAVmUhDQ6uF4IeX+udeXBwMXXKv/7AsuMWzSGL1WIHCZfriBigow2mLA5hShtFCU3jxQpf+qjhbpUmraDG3agNoxdw/dyfbufoN8oSq/jr0w1pKYox2GrrrXljykm6c2dZfIyYDrvcPUz6t0H4M54+Uat1wOpFTl4dbVmoGqPISISea2LYqTIaZ4UtIGTgqIaoSGGs9aiJQrklAjOBbCZGOx8KeKUzYno6IpUWvvom8XDjBGGAOD2ecl8/bTibfffQc5DIisDM6BG83LNpWU1S6Cs8JKlghTWLMgh4mwP7BLKyoHTnlm2Q1k+SF6VCRP+AjrKeFmRWOCQXDDQNJMPp8JzsHg2KsS80JKmeU1oAF2gXE/In7HePUEmQTOnmnYEb0gecWnaBqXrDi8/SxhteYRrM0xSEUgBHZLMrX6DuZwYHl4hQQ4vX6F3JwYouI9DCIgHhEle9Mo5LwCViBol80B1PtMGEZYhRRB/Ymgjtc//IyH73qGR3vkzcfEH94h8QafAFZSGhDxxPXEkEdUAsvhmnC1Z339iptXP0OWlXw64zThJRBGxYXMcozkZWEaFQke1SsQJZEZxFkNMjXfAzcEYsxoEqZxxzKvrKcTLggHvyfdnsnja8KTkfH5NcozxtOR6GwfZy+E6IkoS3zBmiLLEcge2Xl0uiKFmf04Eh69y3K8Y/3kU0ZmkpZcEzrgZGfz6WbGtOIfJ77y5lu883Tiez9ciQk7L3pGndl+Xc644Inl7FTmVI94L7VWmliPbtMqaDWrbTRIc2X8Gx3RQovqmURN0pSO1lhBpc+x546i6HbWK/mqHKUx+u5nk64rre0ivKQ7u422FUSP4BiMzFdAkUs7jq143YVwVfzDujFvjdf3FGEC6H0TlJLTRmhq9c1bqQ6050Gl3So4/mVOdjWaim6dqI6P9U+3sQsF8GS1JNLGEYz6Fp9riruu+SGlGpUFuUY0iE2ZVW+0rINazUmN58CWJ14KO9rm6ue5fg4w0BNsuXyRar/b2Mp+AnUSajRA/7OBgK7jygVStP2V28YzoFdBgbTN3NRd1c4PbDYzoCL0++ORau0umLNX4deGGiK2SZeiaWiORNL7Hkh5bzc9pf2tBGh3b50/7v37woXU7n197GgX2leIi+2XTkqgdyIqYEqL86ZoiTlnc6QpY9Pu+W3OSt/V0K9SnAwVnJYYWjVKuC1vhTyu9dVMB7VZAwKqVopTnOOxU97ZP+Pp9QEd11JpsXrUZlSiKTDU27hjCfEq0SRDuCbIDtWZE451yhw/+pjlR0fcnHBO2E97W5scENkzBSFqsrz9ORFTQnyJUiFZLgAVNEfWVyvxNeyvH5LDARXPcrph/8ByhbuYcM6xrIkQBMQS+1SakgVytnh+EDRV6VAYs7LGkfXhU1SE+fUN3J2JcWX0MIrii9kuq9XyMN8Ec7uVFPHRNCmK4sPAfpqYU0I1kskM7Dj+9DN27o5weIP49E3CRxHc2aqsxWwRDiRcXFjCA9yDh5xfvYKbI3I8ko+Z4HfEfUIHR15BzwveJfzVQBJzmvIi5NERciTHxTQORbqNOTMMA2EYAEcQxY8GEiDidIC7I/N4x3DY454+JH4qjA6QxCmupPXIzToDI8KVFUfyMAyOsEuoHpA8A3fINDE9/Sr66gdIjuAjygqSLEx/FZbdws4NvPXsbd55/gg3vsZHy20QC4EO4nD+bOW0nZl+Ks3QelC0MsjurDaeWf0EKq7/PFOq6YprxI8iJZGbK+feznwFAe3AVT+rZlpWLqXfItBQ/YvKFi+pwM08UJOE1UMq5QxrAQWykalGf7c5qH4ZPT3ZnNu1MyfXezqzQOuzXCCbPiC90aI6r03AKhqHpg2pmoFKZ3re1b0HGl1s91TadMH/Ol5FmbNmEun6pWAFiOzmGiGSy1oLtewwlg9AtYWtV3cS67IJuo0rVhBWnUNbv6QL999AyxYa+hdfPycY6BBam40OHFwwsLo5yj/tf0+QS9WmDt21x1RpHvR0E183RvUNuH9ddK9ix7rI5bcGNGRr76LfdZx9O2z3QzfRnafuBYK3Ni8qaWlxtKlgp1CEPsOhSQeZFitabXTFrrQBjmLvU7o+0Tnu1GyMbTddHJoqhWxJlaRtLrsytaJhI2RNO9LEfqrDZhaDR74WOSkuCb1Hrb3abb9jzmMNDzUtCzhJZDyPB3j+8Ipw2JPzCdKn5DgzKHgZUTyqseA0B27BuNGKiLRkkznPnOcVl16zT0fSukfPN6xLIupnxUPeMYwjaTpYjgEvaEqm2ltm/BDIcyaJIzpLrbvzE3lOzJ/eogfww8TxxQum/WOGceJ084qRTBCPK5oVKQW21nIeRBOU5FjOB/xJUQ+LKPN0TfQT63wm3h4RTQxeGQOMWfExk2oYoJpdMqXI4KUwF0deZlyyfAXDo4k8KbJW+2ViUMf66pY4XjHsHrA8fk54+X3cauc0LQsqwqqC7A/k85H04iVynkkuIG/vWfyJZRbEXTFeOWQWfJqReGJUT/YODRHRlUwkpxULAR0scsMBfmDN1v9x9OSQ8M6RgmnBJq/keSWPgts9Z7326O3HzOkz1jkhMrA7XDOVFM7rfCLGFV0z8Q68RjQ5yC/APeTqra9wlHcISREZyCmjrMR0xop8QRwWrh885NGDR4zhQ26Tom4CEp5kpTnEGF4FAhXwbzUJaGdP6/G0Ow0WFxq0FZO5F/9eaU/T/W38qzJHy1hXXI3Ywvs2ZluFpMLkCr0y2ltoUdEyVXLRhB82INDLWZ29gkYzpdL4epff6HYV/lU7cEIdRPdM/e4eP2lkWbZbe7rbAFKh+Y13dDRNinTfhNl7i/O5OZfLv+vtF8KiXD6S6zxYn6RBEWluaZV5i5Ssr0Qy2rQUtiUqo7fkRWhlGVKi+QoN79ejahe7okpyQfv/4uvnSDpUJkfzvcYrY6tXnygIpDL/Kn02jUCtSlglRi1MsvgU9Bumbsre27QhNvspuqnOKlNTyjsK4q7+LRuKLe/QGiB0b9baybg/F/d+XsxRe7BJw2hbyfL15X11Y7ZIot5M0XIy3OtawzPSulkrZxWIT41lvt8/rZoLsQ2pDXSUexrI6A9kPRwl4ZNUri8lqsR8LyqIa2FRUvMLQrWNCuY85QqtSOUg+6Iud9myDrpJeHp9xTScSacPWe5uEI1oEIIMeCmhpOKKyaD4NCRL/MGaOZ1ekWVld07oCWK44tHBcZQ97BZkdbAueIV0iqzHFyTv8ENgGgZAcCmT84qfrohZGJ0nLjPzYWR4+JTdcIXbjwzBkbLj7rMznonknkJ6icszoguZpQDCCCScJmRekBQgjMBKFM9wjpx3j0mPn7CuK+l0ZHAQsjIEhwvmN0FccGskaMKJN1qULfsiKoi3cqiyrKSbW8I0ME2jZQBNnuQgEZnWwPLpC/xhJO0PxNOBaUloXHApkUtM8zANnF9+hqxKurrm8MYTXhw/4/jqFo0H1vnI1cPA7tE1bngDf07o3QvGcEaz+VLklBjcYHUUZCCLR9Na7KG51TpLCKMqc9kba0ro+YzshGFYGd98zPnmE7IqD6/3uN2elDJxnsl6CyjTJKAD6+zQncfpiszg9EQ6fsbw8AnpeGTcBdQFYE9yk2XfjHfkVdhPV3z1rbd4//kf8b0fZ5YMZpAqCdErA+goyIVo4YzC5Y6xiUgxkWjhMRWY23fmRFvaadlStzNYaUpVjVeHO3PvyWa2a/Sg+Eb5Lp5PhM0ZkO5n+b7RCgEZuKwtAxTfhC3RUh31RndqUw2LoJt5s3m6dfSvvMBIWP28ORuUvpQGq6min2np2qtg4uLqnu9J2v2r0b4NJJhW97LLF89ezM29v93GkqT6hNWBUo0Y1bG6AEktZYyBrTIldtfnTBVsf9ecC41HNl3JFwz089fPoRng8kVfJD3XzUduTKzVIMipgIHye3MmLO2KbGChMqKLTSvtR2OSnVrtErHWbuVu8ih9q56t9d1sDPdinN3VNlFlbnVcG+67nIqtDy1+uDDW+4Bju93GWp00paK8alKRuim7XanSHcjWWXp/hT6xj7bN0h+a0i5yMe5aU337vILBCggAKvOnAAuprgho0cRoNa2UQ1bXo0YkZJwxdFW0ZO46TJF3d4FfeWvg7a88JhwGVveIwT/ESUYGRTWV1MZl3vIM3pPziaxnHBlNMzKvHB4OnDWS1oEsC0OYOYwDOXnUnVGvpDkx7IRRgwGUlFgB70wdzDARlyNj2JPygO73yIOA7hZWB6M/E72AG0oqUsVdeaJeo+c9Pp9x62skrpASLmdkXcjHW7IL+KuHMA44t5B1JL3zHqsk/HxmWhNphCF4Rh+JORISpDnicjKpVM3s4Z3gXVX9Cjp4c7JMK+H2SHYjOQwETfgMydv3eU2s5zuG8IC4fxN3d0TiiRgjwzSxLmdYsh3R/QN468Dp9hW3f/h9ckwkd8f04DHzJ8p6eyY/W9ldP0DcFX5OuNUiQDx+S0JFQkQZptHqE2iy4Joc8YOZYkYGczcWR3KKRkh3N3C44urdr+BPA7qeyGuEPCMqjHIgh0iMmbwmvIIuJ2IuiY32Z+LpxD4ciDKynm8RF3HDAccenOAmSFF48sbI//R3/y6//OTr/Bd//5/w9/7VH/H9T0+szpw2JQtZBtuLnz/Z9eRvvKXQAOdMLSxNAq5nwu65T10b62hOalqYQxEiCmVp0djcu9rZrsJYMue1bq9UIWKjR5X5dhxO7PxrO/9923a2NxrVCW9tJHmjB1rNEYXOa9WsdHRftnbrRDbAoIIV5ulnK2/39oxZzDcnV57TRXzUYW3V/Tpmq2x9argndw/VcXUNUfrkjE8oStTMiEWa1GqDaHEiFkHxVO4gFJOBlLLUiDH2fh1aF4o/Vl2/DC19/j0e+ZddP4dmoGOebbKEJulXxiXVJ6CTKnP9uzC6bmNtC2Ab1AZRtQ/3GF3dtN39FuNaIxTqTZtKbXtWt8d6Ztgf2s8h3e7qTnStE36JRi+7+rm+d/ddpB/5gldtCPE+yDF03cJ0CiCgbKymfqvMupgwmgTTA7mmurPDY0Cjkx5qoSHKK7RmTZPtexGbB59Nw6CZpJsZokYOVMCBOjyCkwQMiDiy2sGYdOXNa/jqwz3/3nvv8Zvf+Crvv/OMp195yHQ9such/tqk3ZrnQNcFBHIsUmw8sZ5fMutrluUlPmV2wRPPC6wL4Bhw+GEHmnErqE4IAQmJOK9mGhAI4hGXiQmiJlyAoaS4njWwe+y5Ox05fvQxwYulHdaAY2IYYJw8ftwjkzkY7qbHTMMV6+1LyK/NpBIt5FFcJCyjJeOJK+eH76APr5Cbl4Sc8AN4VzILKvgIfl4hZiNwaS1eycHs8OXsuZIoTL2F0Om8wHmB/cTkIRVmpuoQp6R5IckK40QeAj47SzQVz7jFowliOCBPr8jzic/++Pu42xumaSAlhzufkWkk3c0c7z4mvnPH4dFTFv+UkRXyDaFkFPRAct6Ys7OogkHMF0ZFCRI4A34ojDM7cwVJxes6rqTxQDoOeI6WTlkdEjZ/moCgBDRncorUOio5Ql6Udf6M/f4Zx9PEeveKwd0RhwFxO8tdMDxAfWD3pvBX3/hdvvX1X+Kv/tN/yP/l7/9/+If/+jOOuAJ6U5GerQJkFpAsLTCgN4NugkQB6vU4dlRiS2Rka2ltm0N0Y1qF3ntlq4tQwEEzm7S+PQABAABJREFUyzZhofvXNJTWBzNJFsFGSiSBVl+C6oslF3Rpo/V1TPeCJxuTdNvPwtDquxtNksqIq3NDz5ApdKQP3ywamQtzL91V36lstQjK3CqIK9VR6+d1Wvq2GgsoDVd7I9qN5wtARa/RbYCkgEbxxGLG7QX4+gqr02LRJ7UbbYTyRTktpIRqd3OlFzd0vPLLXT+nmaBnovVn35EyWRcotHiI1k2oJRazqtDrs1U7UNFMfyDqfW2xcrfhiv+Apg2VZe3C7Op76wTVTVDj8erfbluZS0hJO63tENT2BTNp3EPIqm0jVLXbhup68EPZ7PaddvNsKkAuFrRWTbvMk8D2ex1v5t4cthbavfVdWpn3xdj7JwvzL0jczBJbREWt3aBiPrOIWliMM+Di1GJjawlWK9wygKwkRoYMz3eZ33j/mt9/+z1+49d+ha998D6HJxM6WGx3SgklIgjZGeB0HhweF4Kd+zjCHBA/Me6esZ6OhnfCBGnGycA4ZkgDOZ0YpoGUM96N5LigEXa7QI6RlNZSjTEzhhEQ4rKQ/DPc46cMw0DMP0FOgUkPzLd34DNBVlJaiJo5ISivGEPgcNgTpx369CnTs3fRFfT1TyGcEe/IToka0eMtygG+9haDE1K2ktDBgWjGaVELJwMS3mFFonLEhcFAatUiKQgWYmhMsOynZYbBkkrhwCfF60QS8CGh6xHdP4PpGj29ZoiZJSyMy8R8XtGnTyEfufvhj3CnE/urEZzaXI6QghJwrOcjy4+PTO7A8OABa3zAroZcSmQQX/yghJyiORiWIlbjEFCKg14xg0l27eirYjUaxoAfDoR8tDTFOeC8K6cnEbxnTnNhOELA0kw7H/AoEU/2I243El9HJB5J+x0+rKRhxzIvhOkADKw+c/X+Q/7O23+Hb7/xHf73/+X/kf/yD37Ax6cr/BBZ04LPSsZZhUSU4IS1MHwr/X3pR2RkSTdftfs24MJmN9qu5exqWU9pvkJKtmJgagmy7NjKRpNQs+r56shcNYbl7FcaneGi8FrtS01x3sjERi8vIoO6yxXGfalit7bqSJW8AZYWHle1wRs02jZ1P6bapjY6dHkfHfgpwKiCiipMNdDV+zpo1241m5T/1L5uC8VfeHUAIVK4ldoYDRJVHwGaNdnyP4AmG8MGyDpexP2ItdrfyqO2774sJPj5HAh7Zlff0q1Lm3zFVP6VCatudrW2QLUCXzEplNKfbT/1k91vaqEw9ERDidAN3gDHxcatC0vqPqvOLrXRivR6FAvkEqtZ23dlsZo9S2haE+rYKmNuJ/Vysnp0l+vzne2oHoJ28IS+UmJO9/00yj0ibLkFuvm7WKRuThpY6qoh1vKc3bmp7TQ1ZZ0IfHG+jYWOFA2By52Wz/w5oitSX5njIIGdZH75ycB/8q2v8jd+6Vf4yi9/Df90IoUTZ43ASBCBMCME827PCVK0/uaERHAylOQxCRUhDA7VR+ATIUzEmPB+AF1AHClboZlh8Hg8OoykeYWUCX4kZ2cljGO2Urhhh0zPcW99C/fEs756yXqj5LQgUbgad6T1bB70PkPO5GjaC58SyzyjYeD4+paHb77J9dtvs8iM/OwVftqhayalaHbiq8esDw5IPOOXlTA4vK6QE0EUSUqMEUllLjThaxZuFMT8bSztqZmdnFM0m8lD5xnZTaTBI94iP3yOSA7kycwJmhMSdgiejOJXiO6K8+HA6iPpJz9DPvqEq0FwwfjEuiT8EFmc4NXDBOHVyvzhxzi/Mjx4ThJHmG/xWKiwr2dO7TyJKuIF5x0pK4OzGGvJapkQo2WFFO+Leh7we1CP04iGAUSK+t6hMeO9M4erLOBGzrGUlHYeDTt095BRMuurZxxffoSLMzo4ZBgNWEQrgiay4zw9wA8HfuH33uN/+/b/mv/Tk/+C//wf/EO++3pmlMFAEZ7gPKRSyjtHpPjDXGThVIrE1x3xKq1TjYkbXbFHc5Ex7EynbJqhPlPeVifhHq2u0UGNHksJxXagASvr6JoUvDUh1NwFFxFFtVP3BJb2jNC0vlLfe3Fbp1EWA7uWQqU4O14YOjpG2Jihb9/UyKXWgY4EXlK+jplXAafRwJ5O3v/ZN1Huq3xMunvlcm6klitun9WKEpdOohZAoM3i0QCU0DH8ymsKRNTuM7pb6ruBDVT9u44myLXb/SJ1CKlKw73TR3MI7FEX3UR0bfWRA/fVG23C66K5jmnXZazoMHc7oajzle271tceXVZNQ7/hukW/16/t0drn7pBUiCdSnHsqk7+3aEoxA2xARyqAaN3oNpidrrK5KmK6z/TZkCx9GBBffH1ur3fREfXpCmRUQDvwtWU2QFrCIcApudTlzgJOcwk1rTm5QVk5sOe3ng783W9+g7/xN/4KD756RZzORCLkgCcikizMEMVlWz9JGXJENKFpJaYFVAlalUGWhGmdld2Tx4hfLRrAlblxiguBVpBZzQvf7wbivCBJGMNgEvuSkTWyZgjPvwZvX5POLwlpZdUDEu4YkpLzwjR5clxxqpZUZih7JQs5gc8ZmWfmH/+IvLzNwydfRV5F5PZjZFxguSM5B4fHjGHHef6Mnasqb7UcW1nRNSFrLvtnM8PkFPHOG3NIcRPkql26AGhB8GvC+QH1Vl5bcyLkgTwG4jkzsEI+k3Mkeo/EHccPvok+ORA+/hh9/YLp2uPU7PviBT9YOpRRBdFMQgiPBpbzDcsnI8PhhB6uQCO63BnIcJBxFq4XE+KsOFRjkiXDqKNISmplaDMJWVb8QclhMPV8rVaqm5e8CbQGLrIElMH2TvGhcc6zCoQg7K6f8tn3M0O8JQ6OEDzDdEVcMjjLZqiaWMLKGlf2v3DFf/Yf/ceMqvzv/n//iE9vHNlHSAuSPKkk7XIX4cSbo3KF/RXDXxY7qsJJz3y3svDb51IiCHuQUc5tVjsrwW2awkozG33uaEORW1rMepYthJsSXij9WKpG8ZKUGtmokU4dLZItcspIbSFyNbeAE6pp8gudG50JIpYcrjLhbW5oeQXKELs5/2IC2AGIpi2Qy+//vOdqv+pLal/6kMMmvBb/CEqIqG6coBmztYq19bQXbbG0XbKNM9t7mt9EHUctDdB3rfmrfbnr53MgrEOQe0z8HnPsosvpJd3mO9B2Xnf1TE8KsqzMuC5qdRgh0FBA2wIFhNT419qdxtCLGaEuVE26gRQ7PFzYx5qqvXeiYQOVZdDaNrxu09HFnG4LURxnKlhoT2zz8DnerNsYLQRFyhRJl7Sj2wRtE7p77W1OhBsA1oY77Ba/oWwK4ciXuQw2ECdtbrTlgChrkTEbrZj6L4s5Fbp6UJLHI/zC48x//Eu/wO/9/i8xfnXkpApJGP1AZGXJ5lzjdTUHt1reMyUrUlPD8nKGuMCSiMlixQfdm1p4mIAjPkW0xLVLsD64AeIpk1IijFaRcNiN5Dnae0UITBbW4x6QH14R3InlfAYSkmerVjga4dOkjMGhcSZFW9bgMmPY48LIuiwgptLPP/2QWXb4B8/IP/6QcB0YeUzUI3l/ICAMKRctQ0K8KxklM6wJiRaeKKilXy6ZNDXGbeXVkh2ppgqfjdg6h6wLEoK162zd1jjjk8friERI51uGfCYvnvjBr6FvvktKn+A++Ygpn0x6XpXBC8kJfggEwYpLedAlweA5BMeynFluToxvPkA5oOtrvDNGrdHhUySmTBgGwuCJ2RxDnXfELBYyWVXaWBRKjisurSQnJD8Q3IriyGsu4zV06L234l0+oEkQX+Tusnc0ryT1TFdXhN1j9PxTnDhyhkUiOpjaRRzocoOerKjTMWXC1wb+o//h3+GPP/oR/9d/8QNOKRE8aIyIOJKKAUMK6Kpn7oJubSLFxdFvJ80VppCbL45WZuus8iLl3FbnOCl0t9HP1pZsdKFnWPc5+j06UunfFkatjaw2bFJBRq8Z1RobUr3lc6GPRjPEG7cgacsG29ObRp2aL1cnHLWx5Pb+yzms9/eT2ntryT3twb3x96+pqveq7a4I7uI5vScnX4K7+5og2LCDoA0kKMbvW3Xk2mxzNK15C3K3nlUT3puru4F/STTwbwEGul62N/UMPpkKtzLEC81AVYOVLFAXdg3zqNw6vvkUGADrJewNsbbEOk3t5Ni8P4WWVQ9ogAHYYv2lgYhiqCk2M7G/XYdSux8tcUZRa5lKSGmepKUwRUMPdcouoh/uAaL7JKFpJ/6cEJG2DytjLyj+4obNS7mb7LbhjX7o5jegYBwi8bmr2Qnr2lXKX/GaSSYtwVEnGShi9sosPB09v//1Z/z6r38F92zhFD9GxTGlkVlf4WUEnVGN5GzOY8YEDCBoTqQS6y0xolnQmNFlYU1nzssNh+fv43bB3EtyKmktIm6wtMWrzvjgydHmV5ypQ8Og5OWEOIcbRnKGcPWccO3I5yMuKzGe2A/g5wHNC/vdyHJebJW9JxfnRrdm1uUGHzzTtCMtiZwVXOT06U95+P67LE7RDz9kcY5BRvw3LbviqM6cxNaEF0vM42JE1xVJiZwWSIkcV9CMDwGwNLxoLs5riqZY9oAjW+4zclxJRLPDi7MEPTkjKbCmTNAT+vIzltdKev510pP3kXgLn30C8ydMwaOrSfNuGqkx0eKs3KoDJp+JeGQMBCJ6zqS0oNOBITwg6I2FQWYLA3VqVR5pUruB8LRGGEacKCmWwkmskANuTWQRcthZuOUaSSWsVMSk43EYiOWorUskiOKw7JYiamYRCSSv7B4+43T6pNRGyAhHZDmieNbg4bjiXmfu/E9Iuz1yeMT+3Wf8nb/2P+IHd/83/vmffMhNslrVTiNJIWswmb5nLvq5k9zAuhS/p2q2U6k1QdohM6ZaYtZccR402aYyjIwlra20wM6oZkVcTRa0+TM1gSdjaK4AqfvERisNaExno82NGZVBCmK+Cz3R0e5vzVvcvCs0p/GL/rUbDfziy23f1fnp4VXj2R3NovLMel8nLLWrdxAsY6xZVKVqPjZosfX3kq/VITTGLZsew2H+A6ZBdQ13ZDKhzE/GNdbUFqnQZy3nehtT0Qz1gKBqxr/E9W/nM1CRksKWsa4zCbScAhVNFSlG6n33FqtXQfXotdo+OuZ1AUdrbYLmgLIBDuuv7x6q/eNycsqzG/isq4OtVHVMqU6CtYHaj4KitTD6i7Ca1lfafDUc9Tlq0CHMi/7VsZQ/KgO/WOBtzjZnlG1+m4TQNkjXr+bg2U90bagCpb6zbntG6gGvgMm0RtrGbkRQUjB3GXV4n3nvUeCX33sLvfopn754zS4JjhH14Pw1g/N4mRA3okOwKl45k2M9zJlMKVe8ruY8FrW4hyXWZUbCFeIcOTpyjOZs6HJJ82pthGEgZ2fPerOhky0dseaIywrjgTxdoflMPEe8ZqYAaw4wJOKq5HVlHCGeM0EGspttLAScLKbNOCeCG1HnSDrjZ4fOC37/CP/yBWEYiC9n0hqxyngCGvFeqWFkaY2wLAaCXIZU9rSu3bhsJ1hWQo9LViTIOXMaFAdpF5hHJUjGRSFgzolptaJM+cc/ZPnwI2T/VfT510jHO/x0JLz4KVNO6JpIp5lhtFoDeI/LlmrYi8OnhHpHUMheGGQgp4QuC+5whUooNvWMR8glggM10ObE4cQT12ROqA7UebwEBAudHHxAUeIcyUk4BAepxOdnY5biTYryEgwURYsKydEyTGo2M5AAOSSGBw9ZP7bEUEtcSYui6wooiwheBkRG0rDixwW3j7y6mvnGX3nO/yL/h+T1/80/+N4nJHE4SoGmVDWVuWmjHVCL7WwA4B4t6I9hAd9bNkI70y2vB2AAolCCXKIKVAs5LgKFZrT6FDXaVQB+pbWqaK4qecBpyYqnneWi0t/7JEhaWxahci9arNBZV+rLaPXtqu8vPj9NA6nGRLUXoBrt1MuXNz5Q/85dn7Y+tFBzpQMe2gGZys8Eim/LRhZrkrSOf11MQE/Y7wGFAnwqh3Ld966xJTMp+PJ9oiAHtJkaNmF443m9xrYua6PbbdL+8uvLg4HCa5s9uf7dNABsYKAWIsoKGu8BAPvXzADFtqbNtl/nsyo8K5ItE9F8BbpFbQvItlGara6+s3fyUy6k9YuNXdvtgEiZ/IoGe+cNQ8C1TSngx239qw03jYRu01Df12y6lcv381nbqp+XhB/Stdnmo+yB8vxlyE9l7P27+8+NTFXbXgsj7MCMnYsuzKdpCfw291KKHQlIjR2W+pHw5gS/+fxN3n7nGuc/gbtXnG4TeU4gxrSH4cA47RmmA2HaEcYJ7z0uCJpNKhZRNCnrYp7/yFiSWEWCH5DBQ05oXvBeyJrA5Va4ZRgmAwAK8ZzMkRNTo+O8eYOnzFlGNAiyntG4mu/BYNXtJKgVtylJc8bgSHNiDIEYCyNTIcdoRWRcZtjtLHxxvsG9eo2/Gsn5bMEwe+DuhC/bwedcTDVmGnBrRHJh9Ji9uJbTtT+KdkpBU8Q5ZcyOGUv44/Cc/cjy8MDt6Y5pGhndgKwrXtXqNHzygvSvv0vOgemtp8zHE/LwIf72JXr8DOcyKWYGyeS04P3efA8o5bqKKtVlY9ogJOdYSeic4JDJIcOMOfHFVJynbO0Ej6VZceR1wXkpoNIThuJymE0LsXqY9tekDC4dzdk3LZZCOuxLCGomr0paV7wv+q6sVik0Z1QSg2ZzbR33pBgRnS0yASnJoISpnmMX8D4gwep4DmRmf8Ov/fp7vPrsr/Hjz/5ffO+zlaJnIoeMS9VhcKvrkbV60XfAvf3eA/BNfS6y0RWt5wvz0ajMW5zbBLQCHKRkxdOW0qnQr55R95oABckmLefGcGr20Apnyo2VoDTG0+CO0e7eIbHk4K8kcAMjZc92jm5SaGHzPci1baA5OxeCV3/2wmoV4Bqq6P61W6tG4NIcul2eGnYpWgAdpnXasEflFVVzUuak8exOK1TpdC2Kpza/oUVdmBOtw2hml8uqJHEr56LTaLSdU8CeOLctBXXu/12DgVpvoDGh3mu1MrK8Mbvc/620AhG9+qI+3q5WSovGBMU28paJS7pn+rCP+mFloPV3LtcXto2r3d+wMcVWfrgw1Raqki8aMzt+/jzw6u/rNkzh0Gzmi67fvZNPnac6vxd/V7WQdnOh9P3qMcLFfLlOpdZPWdlU0nYwRY1Y283bnImROdVEC8+pa6rbethhsXlTMuITu+T59ccP+L1vvs/TpwM5JmNmeYA4kvVMyo58vCVygw+KCx4/TAyHa/w0MniPFKlDREgpISmjORKzJ8uC5AcE79AUjdF5WIoUYOfeITGQUmbwgZVIVsUlcM6Kv2T1JLeSs2cKkRRXRGEIoYRuWYW9WjVQijo+BCEuM0NwlnY3WRmnmKPZ9LMSrnZEnc1UkeDg9iwW/I6+fsGYf4E8Dmh05mWtCRGr/uc02lnM2Rhv/UmCuG7buJgVoo4MaumE8/SA5fqaV9//CP3sNXo4wgfvsxJ4vCTSD39M/MlPScdPOXzl10jhQMqmQZCffoy/uyM99OgaIUbUByStpbptoQES2i7KqNn9NRNCIEclaWb0AaK3tNNpphXRKvtcnLRQPO8GU5Vi2iHnvcW/Y1EqYZrI8xmdM8xnWI8MYUS9FVnKeWY+3ZE0MUw7UiHExhotWZW6FZIz88V4BXkleBNSsmK5MBR7p0RwvtSRiChnotyxe+74O7/7+3z44ff4P/zjf8k5OmJcQIfN1bae4cqkL4iGXPzUi8/u/X7xbAUK2g6/lDOes3mob/hfN1rZCyeNxtistLOLNoe1Wla9PVMdmZsqEj6f3OZe/wVaGfWaybY92wOi+ks/5p6eQtVkbMXYSj/bazuae0F/y9+uzpN2IO1e7z/Ho+ra3BP0SpSHvapnLBStT+Ej1Nwrrq2RAbvax+pUatUOUxEGnYr5XGnJDdKZNKT4vlV/MhPkKoDUvit/6fVzmgnyvU/UElV0qmYBU1nmAgByyZTVQg2tk33lOwqOrvHQZpvp7c2bXaT1pf4mUP0yLz0v4SKUsI+brdJwjbd13p5VpeUG6DcX20/pftPq3dl7DFfCWBl3hXg1c6FAHy/bNIJankUKriofXGgMBFMH5suxXGxMWr+t3UIsKsBplKG8v8dWtTON8ZfUw22t1JywsA2rGu8Bs0LMa4GT8qzDgUSeTo7fe+cNfvMX3yJfv2R+dTAJNkTCXhGuIA+mQk6LScxrYpmPLDcnxHmGccIPEzKMljLYe7wLJElmKhdt4ZqiCZ8zOVsaXOc8TgPgSEvpn4v4wZHOCw5PihFWgRRJ3iH7CRlX3GwMyhzTFBdsT56W1JzEbPkyznlYzRExY3busagIJa3Izcww7BjcyLoeieOApoyLSvzJh8xpZcrR3lecJ10W8prxKVu63LSiebVkOpoKIbd3S07knMjRg4uQPfnwiPzkbeZPfgAff4IbgHSLvPyM4Z13uJnvGD/8GG5e46bHrMkjaUBFSOst8upTXJ6Jx8ywQIoJ2TnysjJ4z7IuhGEyHJ2r5GQhfl4wk40LSJhwqUizccWKP5kWSnxJVy3mMOsExJs2IMeIc8HCCcXAjle1aAYB5oV8Xgq/KeWQk6LLCvOJsNsj4nCaTVHpzMPdaSiSNTjxDPsD6Hmz1Wv1UyqOfFlJKRh2VyXO5iQ4JcfVNzz/s9//n/D3vven/MGHd0g4IMzkkitevdGKrL3Kv2g9tTKl7hjfNx+0U13PYGVOmwPa5hhd+ZKW9hNN0lWMvlZJOatlL6oqvLJzDQiY6ayWKm/MTiqxEy6IiEjJBVG0CY24bWRCqrYQKZqDvJUBbj4AhW5RKpz2zE2NLm3hitKRwQ5UVTrX+ktnRa6VU6vvGGx2FnuuaoKFrT8buLhYqM03TYvp5oIJFy2Jbr8ncW0tTJObacXcCiigsCPRYjYsPcm6yfqa67oUrUrVOjhPdfh026D+wuvnSzpUQY+rSNJUmJvkqoVRGXGq0QNaHJo2ZFqvymT6lJjQg4TPhxWyfdcBANlWmYZ++3dU9Fnj22p7YmCjocy2l7Y+2LpvQKiG72xOkrV/2v2dilQn7f3NuaObg4sqiRcmFykaivqebv779ejBQNNsaHe/fdbec4Gwt/AmqPtb6g78fPvCJoHcW8Y+leeWybAiD4Hs+IWnE995722Gpx4XHO7gWNNjNMwkORZVd4YgiOyQNEJKjDEhKaHrTL65IelrKzM8DDDtCLsDbhxLPLory2xqaskO8CZJq7f488IEshhIIJtPS5xnck6E6GE9s8gV7umIDorM3mreYyFreCMk0+RIc8RLDQ5SJGc0RVQjfvSsp5MlpEmZME2IDJxPNwwMZLfj9sEet3gYH5FTIH//E8L7T8ingRQHvJyRuCLr2arsSSJLKupK09BlzUUFa/kHXC5x21lYrx6R3v6A9Oozlo8/4hBAyQgRd7pjPp24eu8h888eML74BD8dGKY9KlalUdIdmm7JLhLOM3LO+GkyZp8iuiz4lBGfi/LJaiXYlXHizUzjYZQA82JMUmdwwQhhtoyBUTEmnjLOCTmbP0cF1Sql3kiO5mRFtkqId7f2TjcRkwNJuKzkNSIuMex3LMnWmWwJX4gZP4QCWJU13eEnj0t7cIMx7gIm61mpXvHV+RE3MIQBr4Gzn/nKb3yb3/yvv8G/+ukfsmB5B/DVp0i3Y8F2ZvpiZfWqEuXn8YC077V/tmYl1OJjopCk0t5UmHSy3AIZNpt6mdvKhLXQ1Fx9HUpoXHH000ZLpX3fnA4bOCl0oD//0CSR5mDe5tPulyp8wCZ3SlV76/YKKbS0Aw1lUrjwHegY+3aPu7xftTBSbf8uwiA7JmzN9D5S3esqc+94U4UzVfCt+0cxg0jN+eAb3yjJ7csaBlfjQqzuShNFL9LK1zkufaq0PudNAPwCkv1F179dNIG9okl+rTNa/QXS5Wf9PRdSfm9b3xj0luWujnfzXu24dfezC7urEuwFk4bWWJsY6TaPcLFxmiqrghXajLbHa2Wofk0uEiv1r3Ibb22bXC+BQc+kG1LdbEvVh+HCB4AeJNQDCS1xUDu4bN91oEW6x7arQHSpmpz+Xdq6dmHuaeq2jmQWlF29aCeErz3Z8857z/CDonlgDBMatISSDcbMyrz64HFDRuNqvgJrRmTEqYclwUmI5zP5dMSNdwy7K8sPMAyFGJaCRSKIC1YZr2qsXKLliEeIakltUlwgWYhenFfyNDHurIyubV2HaCyKHUcWCIOwnqJl+kNxXlqAS0rGsD1F0lWLMojDAb97xnEU7p6snA5XDOe3TDq93hHSTP74MwIOGR8yRBhUIYrlGfAJl2OLYLHaUBbOWZmXJEF1ARmQ52+TQ+b84+8zpjNuJyznmTEnljAy3J7QN64Ij56R+AGjub+RfCaLMq4zupyAFVlmUsy4MSBpwcUB1czoRzOlOEttLGQL53QOX0vlhhE3n5DTa0Rn0ITDMki6sr+k7r9sCYMS4J1NaFoj4gIkSy8MJaP7mnAxwzig2ZM0E4KduZQFGXf23TzbMUsJV3yPNC8GEvGIrmZ+0mAaT1fLZZc0xgmUhOa1aLlHNIHfB7JzjG6EJ8pv/sKv8P/453/Ix3dWayGLNj5S5REtWVJzPSeNuV+wru7KjQ7WTISVXFwyHcoet/1B9pbOudLiCgx8VW0no2VZUS/NRCBCkTItAki8aUma8vBCMr5HV+lokUjRMJTBaWHk4pqwSBP6qtBRaHCu81EU7CVcWWq7VUiia6de/d+NZEoDA5sppKttIKk8k7vvewLZ8a26Jv2wG22Vpiy390shpQIkKBrMSl9rHhDBomoaJ8yQSWRXAT9lXnJbc6mAoYZmNhBeNULa9fcvvn4OMFCRpLJFDtQB1cXYmL+FDxYtQXUqFNrGkeo0KMJWjbAuulAdCze0VSa7Q2yVx9kHHSj4Iih0Ia12v2rdiJ6iK2wItaqJ7F7XtWGnuiHjTlqWogloqvxmd7INsSW9km0vXyCHrmuVmefCiLWf620c4oQLH5jWgNJqX9cDdP8eV8JRqs9CA1H5AgRtm722y9bnRtC2P7cDaha10UV+4ckTDo934M4E3YO7RsdzUytLsEx7pvky6UbCAGEkD1cQM3la0GVBU8SvYqrm85n1vJIcaJiYDiPTcCCHlQo2vQNNa+m0EUIHpJTx3pPjQhg8KUJiZk2JcX/AeUFXaF7cqaiUXSAnRwiOYRTykvDeKkDmvCBEq+94XhkHz5xWgghpVuLuCt5+xt3hlvPHJ5aPMst6A5MwzrdwODHHK/ze46c9YXrCzj2win3pM3b5Fl0TOUfSquW8ZXO2I1uVRU1o9LjHj9DrgfSDH+OWlxBvWD49wpJhOrBzgXy4I98+wb/9mPM/G8i7QJ7PyFqY2OkOd7rDu5V1XZjCzpiKemSdSautnRt2JAU/WYigE/AipJJdNISBdPMCf3oNIzg/kZ0BBXGBlCrTKdoVhx2YolWUnEsRJiGpQ9ST14yeZ/Y41mSmhZp8KqugPhAOYzE/VF10wnlLd5zSHU4HPBODH0heEPUWuCVlk1AAltfiA6IMbkR1IMrCOJpuUZcEV5lvfePrvP3wis/uzqwUQICa4yN2r5SESrlnKPeO5kZ7csPom/DU3deBddN0JnN4cwXulnDPnAsBcpVuF3otG10xpuKKRaE41FZBoPmF1Fj3S1psDuYdIXAYgGhmX9NciVbn8OJvUx1fa1XDOp7edABUB/X67q2Y2v3L6N4Wh18BhzQa3hzhL6Rr04g22bUb2xcR6CqeXdyjfbsdUKvPl3mQQmedCDknM6Ulm2MttMY1/wMpgYT2jo21m3moiuAbL6tdKc6PX4wuP3f9nJqBSu1dUVvotpEugEFnKqg9q/GRKqYabBu42OhxtslquGBl1BroxNE6bHt378VZkWjbPHVidGNebQxde2Xz1lCdGvIiZSO319Zn+6QagklAqlSvWOODebuv9TaB+AtziJZN2+avaSrKMJNuqJ6yMau6X2nMXpAtBXC/Z6udzbEx+tpxL43Pb162W+RGQ/PNrla1FHUOO4JS1FE5bz4aIgWgiG3+h+7AG9cT04MdOkDSGec8YRpMhb840AHkjPOl8Eu2eRURXDZmm8KAjBbnrktRx+cVt0SzpZ+PHM8/M4ARBtMKJGFxAzmeEZ/xJS8B3pU1VwjOHPeD4vOAQywjn1qYW5LIWPYJyRzjRJypsV3ASTIHQcmsaUVysj4zsHIkhIG8BM7P3iA9f052C+vNAjcjV8PZ1NXxBOcId5/Czxzr4cD8+AnD1YE8DKSnT5gePmT87Gek849Bz4QkzLpYtIKOOFZyNG923BN4/6vE80x89Sn+9Jrzq88Qp7ikMIzkeMItt1az4Poh/uFTZI7ka4V0xhHh+ALWY9lPCzlbnv8YzV9ApMTVryfTgiQL6wuApkQUzzjswCvxxQt0PLBcPyANexhGKx5ThAYFdFkIboU8I+JxTohR0TCUCN9oaaijh5RJyx1nJsgj6+jJozdNk2Y4eGQn6Jzx6ogusGpkcOCjFczCBQieRDbfDLCMhsmAsqpvdEe8x2XTJKw5M+6szLWklaCO9Tzz9O03eP7kIfnDI16EtTjLCZaRUFGyw/IhKO1Mm7KiOkB3FLxqBctHzYOotCmVLjVy48y0ISYoSDKmI84Vp7RSv8CLvU9jkdWCkeMKyKT6/SRjQT1dqMranM30IFU4K74CjYZ3PKF59G+mRMGKYW3p60vdglqArpHHqiZwTTOiWoHNRo42+l/yGNyfy/vaApskm4Pqw9HoW27QowdblUFX8zLVeV4Kz8ltcso6qYHn7C9kdO8En425+wQub6YEV4VHAVmz1e6QOv+eXPMziEWSmAYqb/2sJmZCZ4b6i6+fAwwUW0mde60OEB1j6kILpYbOVI0BgknfVT0HrdhPk1w7KRq2TdUYHxY6Q/VN6IBAvXr7O5UHbghr0wZg/WkibUG30iNB2cZHGUIDE7LZAZsDYXfvBehg+707/BfS+59z9dqAC7ujlv43LNb11dX2u3HXLui9d12YSO51t/lX1LmoQOQSdTQM1tMvKLYtC8vxo7K7OuDCiARBUkTSgHOCjA7y2dTPsieLpasV55FsNmXvlezB+REBkl9xPuLTgKYB3a3k88zgA8tdZL57zfj0IeBQt0fcDd6C2EmScT4ToxZpfgFRc9grGhu8wweLZXeiZWpNMsVBytoIWfCOVVZyjEhc8QmT2pcVCR4flRj3nJ895/zoMedlxvlMOCm7oLAsuLvPWBfLTyCaCfOMfyXoJx8Tn7/F6wcP8BLIhwe8fONdvK4MH/0QF48oC5oS6heyG/FLkQK/9j6ym9A/+zH+9iXxeINfI+ogJ8ELpPmEn89o8uB3hOs96fUrfDyj5yM6zxbilxdT+/tIJJDTYpEVqSQ51tESBYW04UfOiO5h3DM8fIv17gZ39QR9fE0OEziHjgIyAsnoXLBET6dlwd3doOsMWcnxxOA9WSFmxe88mu6AQN5fER8/QoYdKoIbByTsUPVoVo7rjDudSbevSnik4N3EnDOBQnTVockKY3lnmRtTTkgxV7oC/EQUH7QkwjKn1JyD+XKUs7g7BN58+IRRPmQVb/unnv2LAwL3nQTFlUqR2uUfaPcUYYXqz14FKTt8OadNo1COvs+UfAtqfhtqJqvmeNYLcmDJp8Ts+upKaKCYDwe4kubeaH0LM85Vwq/0O+Jw5jiZTQisvUyVDDetcK9qL3H8WsrbNyGw0BvZZH2o/hQ1C2o3qUCl7ZWT1EmvjF7KX5T1ryYY7ebiiwljubcCgvpsrnygOE9XP4xGoAUnsQA3AwBDVkJKm+W4kOJcNH21/zXzZNWnoCVkWZVUNLrV+ZDcRTYUJ9kvBwX+bTQDSjedBTE31fIGCLQiwdoxLdVUqu5NzLGrJQ7qF6UOu5fCKSi2LnrnNds61e7dPq42aOt+cYzrGHp74F7YXWPYie4+3frat2kPdJ+7Dji47eDXLdgjtcpv+6vTU2mLSYUtwRMdCJP+9u695UOlG3P57CJneGmyAhMK2t58ZMoOrQe767uz9jUV6t+jgrKWqgJJ8N6c0azcoJj6PwZz5vOJYbKkOHkVvHuAEIGEuojLIHliFGCwPnjxJKesaUEXRxh3eH/AJccgM8ePX3B46ynZB5IIwU2oH8msODK4TIoJ70YjwCSsgl6u2k3EeZxYBTpLr2xESUTs2ZJtkJytdO4poafFUu2qw/mJlZUlDfgHT8lvPmN5uSDZMbjEqBHOn5LuXuHXlanEyLthgjHgzieG00vOP/yY8JVfJO0fsZxnkj7hwRtfI/hr5KPvke8+QZdIdgs7TRxdxvtnjG+/z/z6hvWzDwnLS/Iym0d7UoiKSysSHOt8y4CwJpC4Ek4zeroDv0fyak6C8xl1Ge+KirsC1HU1shsC0cPoAympFYBaF+Io7B+/xWk+EddEHgfc7Q2z3IIKXjI6enR0+N3IyB7VgJ9GdHwCSclZSXevzeafnAHK5Cy8cR8I456cF5Z8a/vyCDJMRTM0EqZgaZ4fXSPHZ8yvPraIp3MiDzAUL35dFzNPqGkEnJrJ0uoeOKRIgOICOVqa7eAGoor5jKiFKIadZzeNWBqmgXqYtqAjaWQMjNjn/mxdMLaiMdXCyESKZHyfYRk4dVLzA5SjXouOZbWsilmthLizpEuKoyZX02wl4TORprF0JXqJwvBClT471lrJYuEBm2Rd+19plCCN2FWZW8r7PdXxrY1LKljqzQeNMnY0fzPhSqNlRdC7LxU34aeUZG/NFt+Biwgy5bJCbuVvHZ+pQ+2SFmkZtzThuYylgKTadYtiKTRFtTF7peQaAHLRfgud4ldTudcV4KBlr5Ye6DaGraDeX37925kJ2ELsYBtvA1WNWXUL1kngtn5VwZ+x6nfVV6BDu3VlG9+utviqcqovh22B6onrGLfee3/7jG7xK1bckJw029gXzcG995bxChVhusv5aNNQwcfluJq68Itw3Oc2dP1XN2xlvhWA9Ry+jPUCBLGN/367jkLYalKUqr77czZUXbJub2xjrna7EtZEIZpg8eLBo6VKXhgcIoFEBvUED1k8wh4nSoy3LMkjBMKww++uEDdy2O+tVO38msOwI+bI4XwkzSv5HC1LXl5Bdqx6R0jG1MSZRB/XlWF0VlzJg9Z02rhizgoIC46tFoJn05Z6F4gpEgZLvZvOjjTPeC9EIuMqnHaPOb/xBL2zaom7IeHijNOMjzNhWcmSSN7SJweUpBGCsiTYpZn43X/M9O7Xic8+4PjiU05RSVcPeZCuCZrJ+xPu/IIlJThD+NpXyONA/uFPceeXyFq0LUXbJl5Z84xFV1pflERMC+l8BycHfk9e7yyj4CnCztTbo0usMiBqqY1zmMiLA+dY8LjhmiSBox+ZDk/R+Ux6+RFpncl5YVXBq+X+PwJuzqxxhd1AePwQ2R8Iu2uGwzVy2LFMO/zhbWRV4s1rhjWTi6S1/OxHLMcjaT6T10hWW7e89wyPD4TDgeCvEH+FTAPDFAhP30KBNJzBKaNYFkRZj6R1QZzgXIBaFjo4sig+WNrmGD34SJAAWUiyENyumEaUO3WMu8BhmljXhEuQK71rtOL+OdrOoys5VbQx3MogC61sCP7y8XYCc0ZdSU4uYn/7wsCLxC2aina2SPiG6osiV4oJAQMIztOk5lz8Fwp9qwzdAMFmemyx+IU25Z5WdIwdMfPs5mSnG83vsJHxhg5AXPyEao40pluiIHqtS6Oz3bOq1LDBCjAay+h9syoQoAcL5aNG04u/W6402IhpC7++wBWWdCoW+m25B2ydzVpi7zGzkjkEVsdJrWdYk7mSlPmslQpydfxGCpHKF36Of9H1c4IBYw5KVVbdk/4bs6kOgUCN42zFbMr81mRChZlqSTyC1oWxG6Wo7Ho+Zl8JLXHF595bRt/qFhRgohUQVM2Etq/b3mvvKG25Tl0l3QD6vjTIZoxOVOgCZ7cx905A9fmiqmtZDEuMqpbNAJXnlx1Vx9GQainAJBW1diCo/dPt/ovzId3vdc9qw/Y9wOvvbFCwSTLdO6qWqN+AXsipFNtRsLxbHvEDEhbT3qjDe8FPwrIuIBYamBVWAjK9yT5cs3v0BH84oMEkw6CgekJOJVnKfCbpHUE8x09/ysM3nzIMO87HIyHsyHevcKNHUyQ4WNbIOvuW9MaVnyIOJwP/f9r+q9mWJMvvxH7L3SNii6OuTp1ZlaWyRHdVZwPdED0YjLDhgAbMDGmgzRPN5mPwS/AzkE9Uw4ehcQgDaTBCDBpsgaou0dWlslLfzCvOPWqLEO6++ODuEbFvZnVX0sBtlXXP2SeEu4fHWv+l/kuxRFUcuUMfSgyByhhC7LMgIzUMEhBnoYsE73HWJGR/dA91jrANiAUjA86Ai4oNkloqi08RSFuhRnAmoD4gWKLusU0kfPprrO85e/AltttLhtUduhfvIn/yLm6tVHbFvt+hZoG7/zL95gJ78ZDQbgjdgIrBDKnFr4pgHagfcENEQsAa8CHQ7S+o9iDaYHYbRCwEi3qf3nrpaJyj7TucrfGDIKf3MS+/CesTWp+y/SOWQWqib4maKIElCOHyMW7oQAy1gNjknfFDi7/eEFyFNivMakl9doI5W1KdrpDlMZysaH3E7PZc/OgX2JsnxOgZsCkkUzfQrAlBUb/HL7a4RYet9lDXtN7jjlfYeoUcnyCxY9ftsSYS2g1OQE0hhM02vTGjN0RjapWdYtWOEHx2cGbgKBACvHB6zFdeOuFH7z9hkBopLu6DMN+BeMiiYQaqyzlSTohZlBXlk5SGIjORMLIipNA/2eUfBLHJ+g6xcN8n3oTikh5luRR5nZPtNFdx5Ez2lPcVOXBAKymUIDAaczlhUcv15vPPMqQQ8BS78TOsp6Niy3Mzhwl+k0ZO4z4wTGeHTe7yCaAkMVnGmsdeKiMOlUG+TbaWit4qMm8mZqXkFeVnl0ogc0g75z+l5NaIHKxfzAaYTHJUAAIm0/WPqjY/c9GIi5ITalMOVIzg0exgyR6d32DHPf/5AjwDsx+K0pknhcwPLFqvKPzxWcnMIC0JF1npakY/BWHJ7KajXptZqDr7+cDih7FudGyUIZNCLw+vPGzJG2X0daXvJaPdEvcu542v6sGUZ0glo9LxvDm8lfnx5QLK2JpzhjUOGyxpVqJTzer0yYDgYP3nb8sM3QqH35U55muLEQqlrY5gg3FzlozfKRGIAwCDEQgJfSfWPJIlYsF34HuP+pB6z5PCAmQmQWJKHDI24EyyuIbBYNwxxyf3ccsVsnTY2hDUZ8+iEocd2re4Tuj7PXF7gbvZ0vYd+6c7FiffxTRH1LElCGjdEPZ7TGMIocNZRzfEZNlLRCSHvXIM0FpHGKCyhhACUZU4dCyXK7wPxBCwSJ5XsgDTO5xe/pvFKe7sNqZv6Y1jpR3OWGwYqENLMIFQgQyplNLEnuiV6CxS1Ri/RcUlPn0J+EfvYzvl9NU3aXd74skZzdESPf+QftEkboXTE/T4GP3gHdon72PwaB8hpEQ8K2BUoc+cCSES6yqBl32PG1rodti6I2y3SGVAXJqjKEMYCMMNDUuGfkH11e8QXv0SLYb90wuGdkesXeoUqH0akzj6hcHYI6yzaL/BDnuazhNsRyUVzliwDi8GL4EwDHSPL+HZht2ipr61ozk5xjYLqmbFyZ0X2Vw/xeCpbcTEgTgEgnGJYKoN6CDsNleYeoNZLVk1C/wuhR7sMhJNmnff7ZFhny3mKltcCRwmt7sw9CFVlpiAdVWqohClrmuMdSlkRIW1Hd96+Q2eXH7Ip5d7Hj7rZ29nDhIIozv3+dyA53OKyt9FyFS0c6MkQ3hNitSMsfgEWsUoiKNwNGCKA7pUDkSmGvlE1FQYxSe5nkIIpeAKkz20OXmvJFtr5vIfdXmOZ4+l2mUSRbaM8iUDG50dM1O0o/FReBNGRV2MoGwgHZgsJSib51EIk0IR4rmKqgADkz0Lo7yerfG8xHuejD0ChGk4o6wdLzNVvUXIfCcRS0xt2bWEAyISQ9ZdJTQzz20oj0Izy3LARIuVkuSZ+RFyaeFQ9tGcOfJv+HwBMBAPHtbI3hTKg8iLlQq28iByXkAmaBkRYmGoE2EsISQxfc/1NqWuFChZrYWGIX2K5T/fbDAFvGeaP/c6R4GQXxgDqcviDCjMdOc4J50uPmc0SP/LL7D5DQhsvM98fOQXJG+GWS7AGCODUclLRtdjLGhMrimekJLvEClsh9NmLoOaJ7PMxj/u6xxbKhs/C8PygowllCZ/p1PnbS3NigqJyqwdqeTr7LTn2X4gqsFphGiw4jCmwWuPSmpSog4qb+m0orl1h+Xde1C50SLRrgffYtln4byFvcf3LaHdEzsIvSPuI+31Be9//0d8+Q9/F1kZ4tZi6hO43oJEBgksBkGcTd3/qgoTWxSD0R7VQJQaFys8ifzGxoAg+NgiKoQ+gZfozQSArIFgiV1E7t5jqAZib1i7QO0jmB7dB4gO62qcbSD2BN8Rcl8E/ICK4glYEzEMKBanirn4gE4C7kvfwdxaMpweI08j7LdI26Bffg1rAu27v8TFPRGbWkD3kbCRlCy3DlSdJaolVh1LIwzREfc9tgYzdEjskf0Ou6hp61MWu0ui6TADBA1sxWG/+XsML71M9/SS3cef4NsLbF1hFwucW0Jj6EJAYk2QAeNrblRZv3yGCbeIH32KSMDaCi+K08gQPc3Q0fk9slgjLPDbgX23o7u6ZHm2pl8Ly5dOWNivsvnFT6n7G7xWqA2oH2An7MVhmyXYSHRr/GaDbxrsYgHNNbZpWBwt8bXDVivk7hvst59SR0cIfcqVkJTlr5BMfjy1q+liet6Db1m4NShYUYKtMVzx+pdu83eu/g4ff/rPuNi23ASLDQa1Fgk+2SmFlx9ymDQnjRXGwvL+FJmjpHJIppJmyS71FGfOydlFFZb3ObcUlxgxMRCNZSAnG2rARFPgARiH4tO53qROo6VvQSgKOMsgelCDBpdARx51cWmP1riW8ru5/EmyzoigMSSr2JRjdVLio0yacgnmOSuTBGZUHCI5836yIpmHrRMYS6yzU9+bcr5FSrI8cbR3kOylLiW1FPCTx1fAnWoqIS05NUVWS8pLChQuAZNnk0IzJpkUo1EYIXekzLJZEsiymDF8hCpRcscEk5KGo0nPxIghyHMG6d/w+QKNisqSF68ATK1uZwoGOAgT6DyjsTwYpt9neQT5hPRf2f2jYi+ulhlyKwpxRJvzazDea/y7Mh0/3k9msa4ysDyu/AAKkhYp1nRJopk5esq+09m9Pg+QSR7P3Msxg9IaZ3PP+7dY5umY6aUbH3PZrJli9Dc/+tm8FQ66oI3jnSHhg0HrISLGIGNS3fOHlxdPE2hUodPIRbdlGAaaqsYMKf4eBGxVwWAIRvFyhL11i+XyiKpZY8hkQARi3EK7B+3w3Q7pbuh6T9+mNbfO4hYr6jtrjIUztURfcfP0EVaA4HDSEG6/Rry8wNo9Q+yRvsebQB2g0+QhCNqxsEU41khsMc5B67HWEb0fhYvGmDwGIT1Tk5NRfbXCHi/QEKlUqAHVARfSWqYe9RFjDRoMjiY1sOn3SegYSdZCJBEOiRIMWK+Yx48x7n3Mi7eI9ZqhEyoX2LXK7Qd3GC4vCfunNMagnSBBGXphGJKgEg+dRpwPGK3wrsFurzGXW4xAbzx2u8HpOV3XUB0vGc4fUdkKsNjo0K98i/DyA7qPH9I+fsiCFlY11cIS6fGhwwwGYy2DVRYxEivhuD4iRA93TtDuLs3N4wR8hsAQu0RgBNSDR6PSmh1VXeE68GHPbrNF6iXbk2sWqxXupRepLo9pdE8MkV5TQ6o6BnzXoioMcgHOwc4RjcMtFlBV9CuHXTSYtaGqj1CzQFYVC3eb0EW0e4ZowAfNeU4OxWFiQHtPY6CqDMEukKomxoqhu2J9BF9+80u8+fEr/OjTGzbnKWMjRo9FE1vi/HUCpLRwnxkPUVMOwUER9SwXK1W7pMRBZsdMstbkLKyktExIbK/GpPCAaK6YADQnDSa64HR+ij7GlENAnIkGN1rbSYnpKNuSlZ8rDPJ4D+RElg/JAZeNjRFgfE6F2OfJmJlsmoc1Vcv6lHyvDKxGuZ7PKd6HwnMQ/SEwkOlcsTOGwrHb5OeFCibAMenLlKCZKIUZSYXS6Sk3S0IcvflpzHEkFEPsqINUc9aEkqs8SCCqhAOk5NVngjORXJb+fIb653++QM7AnKc+zkopnvtvZAucpnaonOYWrZ1+Vkidtcq5jPc7KPcoGjA/sakUkANkWBDyiAoxGZiYKYySE990fHjTf5PrLivB+Sad0oIZCZNMQbD5D7PNdABOxq/mUqBsqgwSyIIna/qynBPGKxu5uJIEYs7GJbv7xtuXOX32SYzrzvzPOsvonf9BOUhOPLi2Ts9EDEiYlj1ff1DH4+0l7b5jqSc4CfjMV4EBbyK2eUB1dIpZJkET/AbfbRINr3o0tISuJ/bbZCnGc/Y9mJAIZFCLGIdxkYEWEBZuhcMSjGCixXgwVcXiwT18v4fNNVxfUImnZU+jq5TspULYbanFEm2DeElNd2LAVRb1gglp7cOQ6vpTOXFK5Rl8ZDg6QxYNbFsWbokMe6IGTJfCHEP0yV1ohGgN4JC+hdAThxYxFtWBvhuwUVJykBGGCFYGuo9/xfH5l4h1xbCN2ONIXC7haEV8+B61dgy9om1q8Vx4H6IqTTTJsBMlNMc0TY3/5Uf46w03S1jUit3cMARH5BR7fEK/vofHU920yNFd6q+8yf78CvPwA5p4SdP2eOfx14r2HisVmAo5vYW9V+NWDSE6Ot/TOJcqJ45OGJ6dI8MlLoCxgYBPngLtsUSsWHRoMWoILBgkMuw6aGv69Y76gWUXa5rzAWygqi3Sp/d1iIo3DkfAx4EQhhT371qwlvbc4RYGW9eoe0K9PqI/OsHdv0e9WBHXL7JrI7K7Rvp9an3sLL7LpYR1AhUiETULonZod0kU4ezsHl968BIPbv2aj57uUZvdv1Jq1EvP+tkrNfPcJRsgAczDTPHxl+kfKbJiskBKEUASFekuEnPMO3MXYASjmimXIRl3AnhQm65oZrJl1LgxcROYAkzcmFWrxSs8GjZFccp0fs5xOqxs1skwmeuAIkeKqCkWeDGqZqIrTTLroXFRGRX9qBd0EltwWOUwybrnr5nXLOYcL/I8c3LjONZcwRHHHjWKZmKhBAgkNczKCY2O7EkoT0/1+SmP65G8A+TGmyWHoiSZpnCW0eQIT0RXRX/9zZ8v1qiI2QKNZSI5kWRUKma2AcrvM0U+lg7KdF2FZNFmsGHGm6T/nqtFnZaJCRUdKL2MsOdlhQd/n30/lqUIU+zfjIpZ55tujgalzG2mEOX5+z2/fuUzAzcwbXzR8RIH5EKzsw4vl65T0OR0RJxASjlz5BGdgRxNzsWDa5al1ufGWK4xAzk627QHIKG8HJpAoyiEaHi83bLf99zG5bciYIKhG2rq45ew6yViDWEYMKGHoYW4R32LDj2KR7oOhh0XF9ewHmh3A7rxiRI4QiUWaw1VZfH0tPIEIw5xFRos0oFpA4ujE9av3MKtTmCxpr88p+o2eJPceM4bussL6hhxVYV2Ds3EM8EHNESi91gF73MyUI7TOlWiqTCnd5FgaNTiakPsepZISi40luAHbAzppU3dfIith9Chfo9imRqB2dy80+BV8OKxoaP75a+pXnyNcOMZHMi6TlwImwtk7wlI6sYYDWIitYloZehDwPrkTaoevIxzlss//TF+37FshK6rCA9egi9/GbtcsrNK/PabGGnxP/8QGxqWiwXhBz/FXT9BhnOGvscrmBwu6a1DFicYOaU5qvCVIvvI8qimqoTddU9YLPHHd1g/uUQ2F0TfE2PibPB1TVisWbiGoe3R1ZrmVoNZ1FTGoeroekP3zOPOHP3yVeonj4g3TwlVhcQkoSoDXpSqcYTsxfFxwGvHwi3wA7S7lhCV/ghq3QGXLNd3qRe3WNRHsHwZbU/pNxfQX2K8p1dLtVzSRc+iXqC2Ie4voNuhixXNeuD1B6/x4umKpWtpsQT1qEpOIEvqW2fvmJnFqmdqf/aOliDAZ9/FsQphfIWzotCSoJdyvCSz3BVGwSg+J01KKq3Ez+6XOfGFVHkg5IRfhQJuTJH/hTAuv/8jT8mhUVZCCqXj4OTyz3H8UZbNZOA8uXGmhNN4SOHqUb+kNRGbPR6FgAlJ3o9pcqOs07EvwlwP6AyYyIF4TwApMLoRxhyC55/ZDDyROEvSY0rfSf5dmJWD5qRDGUHPbFiaPExjF+G8wlFTJYExJtWGjM3xhM/S037+5wuECfyIeObrODEIzhTO88p2VCQye1j556LUhDRomd1juvmkMItCywszQcvCyVxiU7OnUsBJFPQzwCFtrBGRznHq+HtRymbanzol9hywaY3XfH4KZafMNvmoSfOOLrfRGaLT6aHP420H+Gtc35JLMBvDfCiF5OngIuGz383vd/C8J9AyNqEqc8gbd0KykrFDmlQ0A59cwvmzS17yd9AqESZ1Q0e9PsEeLZPV7PfQt+iwR/sW9akcTYc9Q9wRtoFBhOP7DZePdzQ+1fpXYoga8gvUEULAWYfFQOgIOlCJImFATUO73bH/5RXrs2Oae7dpzl6g//EviY/fR6oBCYZoLll+9Zs0tUNjhYQO62wqP4shJQMFTVvLp0oDUQj9gK1XcLRAvWCNxTiwjUE3IVFaDD3W97RXz1gs1ngNWAuigWEY0KjE3o+y1HcBg+B9QHC4IQni9vwCe/8+sTV0z2BxArbtaS+eEW4CWjt853O5VbEWIhYh9oJd3WH95mtcff8ntD9+n3pt6c0R8p230C+9ykYiYXOZOklukxdn/dqbNOtTho8f0j36BbvdNlU/VMrC1oQ+3cOJEhcV9rhB2eP2jtg6bOPwYaD2lm3fs7hzl+GTDzCbG4LfQQyYyrGIjr7bE/qUmxRVGSQSu5YogrFrHBWhrTCrCneyRJsX4BOlok+6KXiij4gkRQwRZyvEQmNrfAwIDVVVUzUVnfE0R2kXt5fnyCIQ13tYBOr1Ka6piU8d/fAh9cJQi2OwS2KzJqonbK+RPqJNRG3P7Vv3eOn0DkfLC7b7UsaXvGdFvphR8R965JLj8nk4kF3OYyJDeu/jqORSYyjN52oJgSpJXuUS0lLrZyRlpWvISnjMFcryqSRhmzTSlBeU25dLMZxKTF8TwVGppjI5lDjK1OxiD3HSt6NVPUKYmSGiB9ee5FBODpR5ZUGRd0W2yoQ/xs9znuTJksmfGQ+OMlOiM0mrEy2wZDd/UtwZiMWc6V+8BGNeQfLsFTe+ZJ2UlqCEbLLMVM2PJyv9qKMcSPTScmAC2xwiTr2qEqAZsUnRfb/F5wuAgVliR0mwGC1lw9jJcKTThMmiL4vMpJjLz/P4vR48mdm9xxNnyDP/QWYHlR02c7WNm2r28yykPY31YMEmFCFlWCWGlu819qkesX32JuQHf3CZ8mKVeUipVpht+tm9JZevjO96nvfoLSgNPuYv1Pj0D12FjM9MJqU9fxYHvASfWfTZ2pXBTPeW7FWZjsh3UjJUnSUQOcP5puPh4ye81b2KXTiGvqNarJFFRRg2EAb8MCB+Cz43IeoHRHsGv4Vdy/XOcvTiawiPsVEQrTBOwaRMZg2KRIfEGqIStMWQEqck9pjQ4+OGOgZQh28rzM0V4d59jr7zOrt//Yjw8UOsExiE+MkTwldeQ6jQQcEK0nuMKj4kJWUUeh8xJmaUroipMLUgMSXzGBPBJRZFEYP2KUEt7ltMvcA6Q+jbZMUF0EGQQYgmd9SLBlHBhIizAY8gvaR+BcOAWMvuUlnXFVF76FIZn+6TSzMMSa4bMSnLXCx2gPqtN/GqPPq//wXLG2X4ym0Wf/S7xFv3uN5cIk/eT9UEywbbCsHX+LNToji69z5muLlmaSU3uYlo6Ig5QVhchdRLXLPEeCFuc1x24+EITPA03hAXgt59CffkPawTemswboWKwUmyvtUIUjuGvsdUOcmuGpC6IsYBNyxpdxuq5THV/VeRj3+FvbnCW4O4CmcMxpic82AIYsFViKQKFcUisaKxhrB1mKOaoYuYsKGixwoMotjFCe7FFxKdd3uFt4o9OgG7wndb2punNOII6hiCYXVmuXt8l6PVu3y6j+nZF7K2/K6X+nPMZMgcUsjmdzq/5snem+TPaB+NuU+T8JmM0PzuGk1MiybHwSUBi0iudTc5LBtDRiOlY2S+k8BYsq0xF4XJdF5IXqxC4qZaBjA7npxLZIpRmOWpkqz4sWSc2c8yE4YzHFRE2+zPmkHEmBhecNKIM2aAgdk5+eDksk90yOn6qefIBATK0HQEVSX7HyFXEpV55qeRu6kGDTlvIIesNRDI3VDL96rJqa3JUzJqo9F7knSYkRwSUAike1pRVAxBs+w40MN//ecLgIGZrkiZDIx81WP2ZRl1Roajm7s89Py3sZRlunZCm0W7zS4FTA2N8iDGDk35PjlFJq8gI5/AXBmPVm5GwQU9KxywTOUvR5VqhenAvOkKO5cyvqTltqOyPpzcNJniTfjsLOdLPb6AIM8dOzt8nFNBzZqPzy0vNWUbT4ROZb7ziylIcXdlL06MKeYUy9rlQzMFq0iNxp4xoUjypjUKvqOuDMtqoLaWq65j2AfoV5w74d2nTwgbz7V9jGwuqI5Pibub1KtABMOQOPp9D7Gl3d2wu97Sbne4raD3vs56aemeCQtZEoxFKyV6qNyCOHT4rsU5my0QR4yeGIZEY6s2Mc7RIuGG2A2JEOeTD+hfeIOT/+w/of3hL+ne+wn+6hk3P/8r7r/0amruFoVqiAwiuQ+BAU0UxEYjQ+iSJ0I9QXsqm4iMxFtq9QQTiFVEfEL4xhjq5THRg9EBfCSEARshesMQYuoHJKlzX+99ju+CM4ahjxhnsV2PulS0M7SC9AFtlX0HIjHlk8SYCHmiMChUtdBXJ9x560We/J//jPbhNWf/2ddwf/d3uakiw8fv4h59gnUDYo7xl1sA6uYOkYF9v6dqPbVxODNQuqNpFEyMiAUvQo1BLrb0vkdcRX1yhg8dblgi1mGDR/0auX2b4I6QylPbKtX2V0ukXiDVAsTQ9x7rPVI1aFWlbpbG0xiDVaHvwRDwVYOevkC4uWblLD5byDaHKEMUpKnRqgYrOJuAy34QAg1GG0ShOToh7j9GY6Tfn1MbJWBwi1NWd19idzlgFwFxx6gJhOtLzNCiklz2Bmgqy63T26wXNVF3iG0SrbO47KaXBCyKgJ0ZPlIUAszAQa5uyAliI60vOovRl2ypbCEWeyffoFJSuKJwaGjECfhISmQ1qeohEHPb4yxrouZEwiIPHGTFrhoxpkp3jtmitjbLazOFfsfEvTLZ5G0YuxGOx5AEUOErM/P14VCWljI8k0u0KTqmCLvCPyAH61uuo0YOIhM65ilMHQBHJJFZbOd5GqMsnl+UEmIGMgDQMfk+eUaMJqAumQETSi+EOFVrk5tgxXmSZW4AVsr2xWAUKsBpAnaqMRd+pVyn3+bzBXMGyuSLYtHZ4hYAkJVkQanzByyzYw1MmXyjtsnXLZ6F5wDIqAALFMwPfJ7sMVd2I2wsv8/Bhj73PdnalRFRqpT7zTbo5/2rTKgZZgDicN0KmtdxnuPkpsOK1X8Qj58dPOYXHPwxfW+m56JGkVh8ObMDs0KYvCtloRMIKOsZx5fjOTAToZBAjayJqmCUOihfO1ryrQcLXn+wYGks1yHwy91TfvnJNR898vzw4WM+/uXHnHz9mhhatu9/SjVEfNwnlDv0DBrotjtiH1JGtxdqadgN8Mo3z4ixQ7VheVwRQwIyGoR212NsZLV0hC4QE2s+1joSoUcqykKzO9Q4rHNYMxC6Zzx77zHt5Q0P/vAPWd+5w+79n+AfP6bfPKEC7BCSy/2oRhiASBBPMJHYKeIHghGs1Ih3SBRcnYRjjAGxNXUcQAxBfGpt6xKHQO87TL9HZQADwxCIXhBv2JtIxYBqEth1Zei7SIyOB/dfpb3+FGcH9lZY3lzhL69w1JjW4F12sxql7wN1NFga9t6z/L3X2f7kPZ795F3u/1dfJX7vd9nsW8w772Hf/SmmiVR3b9Hts1VkLIEdTjzRwuI732TbniM3n1BJpA0p/KDWIM7QuJqw69D+ErdosHWDCQk4MThEapxRhghmvaI+u4X3LQaLqS3eVVSLBcbWhN5T1zViq7QOzhCiJ/Yes1qjBExw+ACyMFS37uDCq4TNU5xNcilIRK3B1DVapfLHIRh8tvBUOpbrNX0fwKQGSdFY/NBiCIT2CnGWwXua+ozV2cv0/hyNEX99Q3f9KZJFqlEhxgG1K2pXUVuL1VTOlkRYMSJS0leS6RbRkJrQoKPumnv6Sl5AkZvJCa+YkifFJJeKiPiMHFEwMRJFJutVdLSvkiE/ACZ3PiTlCYgmcFm4CjROhp1qHncJDxQ5BiohlXOX2xuYrNXi8C6iRqdxUmRaPBCRo2KH7EnQSQYX4yy7y4sIH3+Q+apM/5ABwVSbUHLT4mztZPIOzGXvbDyTPpvNQ1MoxhpJJYSScjNM0ZPjSIWRlydO3meN2Ys0hnknPp50sqQur0Lq7EmqGJGcOmb8f+icAThQVIdAICvoEscSJlR0oIjKhDMiLKdPkKzciMysMFPY5bAEBBIlY1HuOXpSgFj5fv7wx5dPx81bFvKgCmEs+cvfjztwBgjK78yWYH6f+bTHXIdDCz+51tI6JQshj4tpfVUlExLN1+ZwSQ8/szkKU9rpqPdlptxna1eIPMYWpfPxM1uTJIziECa3IOUl8tyqhbfvHPOHr73MKy+eUC87YhT+Xv8Cn7y04V99+A6/+OiCDz9+n2+/uma532EM2KMa41zqRLgXTCvU6wUsp30mIeDjCdW6QnVPtWiorKJ+S4gDRitcHRm2gdCBqz1IRRgGogaqyqX+AkNM2f/RI6UkUMHVC9YOhot3ePIDuPWdP6IZvoYM79K/9wH2xRewXQTTQNtjqoZuu8cOAgTUDdhe6Xc9VIGIo4qKOofzLdJ7hspgTUqQjL4nDh2mgth6bJ77UKVGPQ2Gfd/TB8VGg3eMa9H2IFiWr7xENEK7vaH3JRTg6bY3hNNj2ishnlikFhgCEoWgCv2ALo5YryPP/sUvOPvuPezbv4vftcRf/xR9913Uegaj0F4R1SFNg0qDHTpk2INf057e4fgb32X//Ss0btCSUGwCaiwL56hF8DEiYcDG1GXSakytp02iYTYCah2hWSJap+RKB1VV4awjAs7VRGAQYbGoQQJ9VOrlmoBCDNSmImBRH9CqQo/uoPsNtQ0M6rFREwdB7ZClpdMwcmL0YaBe2JR1H3NVSiUYt8DELeI9fneDSsCGDfRbXHMHaxe0l4/oNs+I3RalQm1M3rUmlbZaTBLWpMZXhX533r64CP7SqX5kM5mJnOm9VUq+VcpTMRTulnJSwfKlsmqU0VmuitHEOaAAETGWVEGXZISEInxzSMOUBDphzO2SIj6Loi8e1lwiXWRN5szNaY9jKG2cx4HMKbIwy6LRCCqLMdc9M5l3YAA+/5lBojlBXpHbBUOUQ/IvIzWBwNiYb35+4YeZj30GglAdaVkECqEjRdwnraXjl2OOFVPFg8yuq0UnkZ6FKZwDMSbjj6krpsn7TGP8/CX5nM8XCBMUBTp/EDpbiPJJcZDR6h/dQeVTVvgzN5j9ncO/z3MECiI6uF5Z1Odc6mOcrGzWGSgoT2Rc4DT2MaY05j3kxR+XYULs05RndcBlSebZ+6PVfqjR03Oe+afGaZdNf7hCuRiJgwWS2bqVl2IMRejsu+m+h9edb7p8ZSO5+VAZSskXmWpwxxgZqZa4wfHlW8prL8C9Oy1SdaQ2KBUnTcXZgzu8cNTw3q1Lvvs7ryP6AdcfndP3HXVTsVo1VE2FVBbruryNBFFL8JEQEiufqS1CkxILValqIDiGYY81luZoyWBbwlawVUoc6/tAiBFjBWurbMw4NGyIfot1kloVR6FaOMLjD9m+93NOvvwt9Ok5+/d+Tf3yC1jrcAIxx3ttWND7NgERhEF7Il32VhiG/SWro1cI4hDtMoOtSbKVgA4dBqVX6JsGc3qX0KwxZkXoI+bqAnP+GB5dsrvpaCrL4CLSR1a3ViwWDdtPP0D6PcNgoU9JiHpxTf36A7ZUrEOPH5KwoEv8AvSG+2+v2P3qfeJKqH7v6/RRaD/8GH3/PRg8LpuJft9Tu4hWDj8EvLbYocdqwA8e88LL6OKY7vIGiUKsQaxgcra17/dEa3HNInU/rFxmuvWkeHXEGtI7WjlMZzDGITaxFsaQQiFqkuVZLWuGGAkx0DRLghiiRpwKPiriFGuEIIou1yzWa+LuCtRhENqhAxmwQ4elAiqoPNaBuIq2DThXpahlpZhqhXQD4j3iDHE7QC+EWhGxVM0tlrZit7/GqEspxNm7KCR+B83Wn4pFZSiimlTqfCjNYCp11lExPWcJzAyLSdkzc0uXcya5OSoZJWerpyoDW0RIjIgJKRFNTLJksyKK2TJVo4cK0BbZmUFDjBNBU2neQS6bmyXujSBo0r6TUh4VSslJ09ECHpu2FfktZf4z1/0oF4sMnH4+/JuMsm8qK5+FurWEREuCZD4/kip8RoWf7zL/bpxCtuCLmhmNPE2AqTC5hiz1i1osl44pyXQKVkyyf8IIBbSkcI2ZPedJM/x2cOALgIGSFTl3k5RmNnkmEQ6s6ue12dwyjUVBzj9mOq4ot+c/BVxktqiyKiOJRAYbY6qfFppGJjAze0mmTfE8YQfTWMexTw/rICdBZ9cf12u6kpD47idgNCcGKt+nzaRl82S33KhzY0bTz49pHM9sbmOG5Bw1/4YNMXol5nBkAifP/ZNeyFLLkr7AEXiwgtfPVrx0+4i4fMy+2yCbARsWOHuMyjGrquGPvvkKZ/d64rDA375PdXFNe/GM6ycbqqXB1RXaVKldbSHxUQUfcCb1K+h9gKg0NgIuNx0K2ZWdOhjSD9jYIVaojSOEQBhSuY6tbLIOF2v8ZZe6+ZFLfNQgTeT613/Gyb3XOHrjTfQnG/Y//AVHf/Bt/NUNfhB6iejRMRydogT6vseeneEePaF/8gnOeq5//gvWp/eRxYoQeqz2OLHsTUq4wwjaevytM+wbL7DrKsQbtANfe+T0hOW3voP2V5if/Jrrn3+Af3bDnZOao7M1cdjiNhvadstwA3GAYFrssx3Lu8LdP/gaz/71XyF3leg9w97SdML97zW4IfD44y3Lt79Nd/sO8vAj+p/+GOM9VqDTwKLNnqDoUdNhbc1gc3vySlDf0p+eUi9OaHcPASE6g1Ul+gEPSFXjnEDwaOiABaIODamNtDXKECNOctVFBvtWDb1qquvPz2ZhHX3fo6amOq6pnWM3ROr1gqEfqKgwvofeEhGsW6TyimGPdTUSIstmwWBBHSmEox1qBpxb0rUB0QWmqlMdt0+hnc1FS01HbBYpd871UA20Q8vqZKBxK5aLu/Tb65REySqxLwKOSB8Gek/mI0vvV8phcKMcLcAak48Yk2+LMC8KyUzycfaZJFmRY88dI5OplByeBsPEsKcqKXVIFbUp58hGJZiQ3rHS2Egk6YJCeiMZCMCo6MSYHAoI+X5JJs0LIMa5lHik0SzzJvf8OLci90qFlRFKf4B56llawsmwm4y2AhiEiSq/TFxG+alMZZjjopVxjobwPGQx48DJnhAZQxb5/NnvyT5MnAAy40dQiRkIpEqRRFms49NM2Edzz5Qs3zOVdFL+qeNiof7RkjtWEvw/o2c///P/k2dgnMboNpnfbNqEedvPrlEWqPhLyqHlYZnZcbN/ISu62b3GMroZwhuTCjMIIFvxc0R5AAZmgCUjuRGlSnkUzOaczypJgiZXUTC/bpzWpwDP+d/H0sm0ixPw1MM5K2k9CJQnPI50xCuzF75c8zOhjNm/ZYnLUChLNaGWMeEofmYw07MSLVxHeYNHaqO8fBr5zn3L3dsLpLpGh8jQ7/FB2GtPTctCKqr7D4jsMSFwdOpopUbtmuHa44eOOCiy3YJLGcvWZkHjFTEdRh1VvcAEpYopwc6IR7QG6YghWWxNE4m9wziPNUIcDN5HQt8hGhKfgVSY9TFiLX7o6YYNNlRJqWjH5a//grtv/R7Lr73KzQ9/ysVPHas33kCj4muHt5HoI7GLBLWY5RHmlTVytKZ98gTOL3n4oz/npe/+Hot6TXe9pyOmNrzR0NcVMVrcyR0uLwYWN9eJoW4h6OUV0lm60zuYs9uY77zFva98le0f/xCefIy/uGKQQNwo/Rb2rcAgdDdKs7hg8+7POP3a1xCnXPzFB3T7HU0n3HpTWB4HPv3ROXL3FRZfe53oe65/8DNku08GngpNXRF6jxhDIGCiYgno0hA00HjwrgcjBNsQvWDrtD+MGDREVHqkynFS7wl0sAwYJ4QgEBLnvbqUAJW65uXOg17IoX7Epv3vfYd1FdI4bG3pdi3GLVOc3afSTbHQZYsMC8PyiKq/whII3hNjRK3D1qlDpjUVYhtCn0SKdR6cILpAgsXVjs2zDdI+ZTCOqnGJorqxuFVFe37F2f07bM+b1Lp6ASqJUlrV0AXLtt3R9qlFtqggBIy6LDZy4lxR8FoaAOWKjCJ3hUnePQ8GnpP1MoYMZJJlRWwW0CGloBE0GiKpsVGMiRbYZdGuEol4VG0CKpKV+2jdJ0Uu1s1kpY7GzPQpYV0ozdmIJOAw2h8zJRnn52q+1MS9MKnSIu/L3GUmJ0sJ4kxHFB00XzhV0AxDNU6ifGa8pnsWdJGs8AIIRqAQyt/KpaeRRk0JvkmEhkQ2hmQHyHStkgNRNOgIDGX0y07MMBkJGVWU0t1wllw/DmG+lr/584WqCdLE5/TDM7Smk/u+WOlaFmpU0nOEW643+3ncFLNDDmJeBYxkF5xJyXB6ABTydWK+/2glj1o03252zqQPs+Vr8tdzIKPjWAqTVVrnfE/DlPFKgWjlPoyKNH3ihEHKMBJEHH8ePX2zMYwvxDh0nQmC+bzmD+0zkoKSJaTIiJ61oOSyDvNxl3BLCZ/EkJMT08arLbyyWPDy2V2Oj6D3LjGfuzoR/URA9/RmwWJxhtWBqJamscjREvWKiTeE1oJPHoHOd4lfOwZSMZaF0BEHk2iHK0fYt8mdrTZxDASLqBJ8wEpqehNDaq4TRXGLBc5ADB3RR+g8DB4Rh6tqMA1hc8UwdGAN7cUnhO0Oc+su1ct32P3q54ST23QnFf3lNbvtjtDuiN6nrGwDtnIsTlfUb7yOOTtj/6tf82j7b7n1zW+wPDtCb1pC7NA4ELQmioflgubJDcgVZtij2x1ut01tg68/hKvXiC+8SHt0wvo/+R76Z5btO7+GqOx3FfEc/E1+PTZKt9zQWIv/q1+wuuVY/8Ftumc14ckV9Tpw+Qth89jw4j96FbNYsvuLH9B++iRRFxshNgE/hOwGVmxOUPLDHifHKRteA1YtdMkDELPwsUESL4BRjLFYjWi3R0yEOtkxJppkTPqAdAPOCpVxdLGl7juoBK0taMSpEvxAiKkcEmsxi4o4BIiBqjL43uNMRYgdRmsq01Bc8Fo1Kbm932Oswy7qHKqJiDFEm5LfY0heELGGSGpCpJXFiVDbihAtNnp8iDhnaHcbuKlR29LvO8Jwm2plOFk5nKRSMSMWr4a2a+kHD6S2zwZHIPeyeO41jfkdlmwYqWRZV0INZEAxiq2E8vX593z+yk8CKMnm8jpLYhaMWXFpJIUCQvpXjCZ+iVFAumx7KPP+I3OrOcWpi1yalduVREEFomXMCRiT/2Um5mdKYJSHmkChgM49KRQ5nWX2KP+m9ZCScK4JLMSxt0u5TQJhULwCM3SRfy58AnN5XNz9zIzNMcysycoXQkKZGjEBJLvybZyJ1uwRGIFULjMkk1MRw5iSJyNgLGNL61HoqBMrISCJZjoRXP3mvTH//PZgIIYEBOZgoNwjFkWfSXfiREqRBjTX8DPFPA8lHCAZZt/NFGw5QGcbZ3aulM2gz41v3FGfnZbkt1FLLKiMY9zQZUAye2mTQj30F+ikoOfDTRebjWUel5LZv2VDlmWZuavKzxlNH5A1lTGNYHe+yT/7XckcLl+MJYdMuGns5iVkd1Te9KNbbZq5mkhtlbvLhltHt6gXHdKt6OtdUuwuWSleO6pqhW0CagzONEQfWVQ15sjQiuLrhn67h+ixQTAScEYzcU9EjUe9SbFe06Z2wYPHYDDOEYaICQFrLF3XYUxqfmXNkMbvFa0rQp+EcG0iQzegu45gFDm6w+L2q3jfI/2e7sbTXZ3TdDXGLuGVF9hUe64eXxAvn1E5WJoIzZBe/AF0PxDCDd3yiPr2KetvfIPrv/hzNh++z+LBGxx//RWOmiWmCizqgLdrgghWL6h2O3a7a8LVFfQBNQ7aAS5/hr26ovrKm4T1Gc3f+h7txRXVB08JTyLbHfQBJCpy4Qgou+EZ5s4R9sM9/hPP7jFEF9gOlotzWHz7LvLGK3SPz7n40UfUYujbiGkSBggh5gz1FOGN0aMSqQAPGAvBJs4CPwyJ856Uba426Y0oES89ilAvHJU0VIPHmAGfhZujwteOOATs+RUxBOqshEIFoVdEHZZItIpbWGLsIFjMYoWYirDds1wt2PkeaZosoFPjq0CquzbOHrpLNTWeSvkcgnOGPniqqs5rSc5bEOra4KsaE3uGCNH3CeiIS4CzHzh50DIsj+gGWLvUNVGiMGx3bHY7uj5PWAORmFoCx1m2e8zKrrD0zV7KUekbGd3QidZmggOjABAZvQJJNJgDmZX+LUpLxiZESdRObveY5bSozuRqqshJVyucBkmxa8g8H8VTKpDCHuVmZA6CQ4NQs2GklHHP5OfooZ0qHia9lj3JB8ZP0UNZtpXvxsT3JK8mL+00CmbDZKavRn1yAAZmOqYYcEUWjyGN0vchhWLs+EQY76+aaKBtXmcDaCYySuWaqfJsjEiX60+PcTLAo0cwebllSlo0hvkK/XWf3x4M+I4xqePAFV0Q2nM3jTErLZe2rQYwIV1jHv+atNjsacwQZ0ZKk+I0oAVgHCIezfS34yUK2CgLmI7Kf8/gZYzDQPZbkQP0qU52PGUaU6rbn904b3wZkeXMhTZTvONGmY/jYAPOxljyL8rXZdkKEBrDDToDUrOX4WBpPgcgjPd8DjXOx3MwVsn3DBMqFRAdqCWyXFjqZoGrLao9+DXWd6mNVXCA0CyOUEmNNYQFRpO1Vi2FwTc4s6KuHYb1yF8gSHL9A2AJ2w1msUapaCTV2oparDOYSgkxWS2LumEIHlfVhIGUzW08A9CYVOsdo6W59yLm7JT6dEXdLNgBzeCRruVo74kh0O02+Jsr4iv38M6z6jvcyRHG7xAfYRiI7R7tA+p7nEQsz5DlGnnlZVZf+wbDH3+fzYc/YvPvfo45dixfUpZ+QfvCSyxeeMDwi08Z/AU9PiWd9RDiAD7FVfvLj6kvW9a//zWGs9dZfPUNnv7kMuUunK0YEGojXC2XVLHDPBuodhvC48juooJlwF85tjvDtQu8+PffQPqOyz/5KVzt6TVxRlgXU3Qq2BTTd9nlHiNWhTB0WFVMCMlQDAPxYofdKLFSvAephdoYnApxSGBSfSC0G7x4FkfHSN1g7AJfrTCuIT7+FLaX6DoBCGcr6jbgSXIjqFDfvkOIDVLyZxqh9y1NbYmhR6QiZmvKBk+tSm8KC18k+IhzQoyCzUm/QVMGtteIcVUSHwMYNbmMzxPjAFapnMNIlSufHT6CmIqoA51/groG1QViKlQaxC0IfcvV9oa9B9UBqIjiMRoyU6CQCMZ09ACkxLXpvRzFgCa64sRAl7PHTapMEBjjyZoV//idMpNXE1BIYlsmRZOvmXRZzrUi5vr9NM7xfBOmSK0qYytkn7xCKWlbRqt8jHBQFCzlFw5Du8W1X+TTJLQ+V6nNDJ0RoAgp5lFOmhuTY2j6uc9M3k0K9vPuOCn7A7u2VGKp5k6+hV1w6q0bRUldCnNuhSpWTErQjEmDptyAOFYBjGRTeQYmewUmVTm3BCOm8MtITnRWRi/T3/T5YnTExd1+oM+SEh/xmMz/NrOdy44rAee0gvkPpYzwNyjKeXx8vFPhGJjiKiMZkfmca+i8oqBcN45fkct9oMRcCtKbsSeWecjsws8vdtnAcyZA4SAhdVqU37TZ5teS8feC8vX5HX2QSDhftxnAKiDn+XuOL0qpr30eHJQEgQwEZ/NL7qyAk4izYJ3LrHsNtlpiGyFIIPhkfdX1CcgCtCJGAZd6FlgnLFcNfqeIXYAGxMTcH15RBowa4hCJYUfgLgwRE6/AVsQhEnyPq2o0RPAp2dWq4IdkYVe1odvvUAzRNcjtu9TrE6p1IiXqtnuunj7EXu8JQ48S0S5inOF4tcaf3oHKYFuPbQxRe2ToiMMWO+yo2i2h8wTfwrDHe8VHqB4/4eh7f0D9nW/T/bsfYbqB/rxleF/Y3epY3HsZa2u6/QA3HqzgjcCguAEGVfDCsLXsPnkG5i85+oMl7uWXsK8/4v73zrAv1rzcnCCdUB/dxT68QJ89Q8+f8PTqEzrf018qIViuTWD1xhGrNx+w/9lDbn7yKZUIQ1CsTaV10ees5MKIV3ayRtrdhmOjWIXKWEK3x19vGWKEQTGNSWBmiMnbImAcaGyJXWC5PELU4vqAVJ543CCNo3//fXSzo46OaiEMfmBYniC3zgjNEW55QtssUre/OCD0iezJ95iqpusDuAqDzx3UBfEBC7mPREicBcZlpZuEZvQD1laJvrppkpsVxVQuVTH4ATFCVdcUeRaz58woeD8gzmL2a6qTBqUiDB7TpPS8zW7LRbehiw4xAzEIKjUSh8khmMcyiteDkubyTk7x+kItLZnaXWaysTjvxjwEzf9KFj5ZVhxw9MvUpmcKPxRxnxN4RXOusYKEKZufnB1vLaOvQjXJTTQRvKnk/KpMPqSzkMHM+zsXs/NxF/K0MsARTMDED1C+HzVn+VmnuUN26WdZOBLjxZmsK3Mv4GCm5Mf75t/HirQwk5Pl3pNcLZ6qRBVYZGkRo5qpBcq452F0ZveEkuSIklo/jx7aWcWFycaalj0eZ+P56z9fLEwwwaxJuY7Kqyx2dkuZ2eKX2HY5Xcqmmy2wzK5XHuKoyCQnlhTXlDDRaun0zMqnINVRO2YlPxvmdK9ymEw8E8SJ7AJh3hxJ5oq3DGV6i5l2dZxVMDAdPJ/j54IBmTwMs37noGOW6DjHfMmy15Xn73WwKPNbzEBAyU6G0ZNRQNpY2jMlr0zVHAkITOieVJttLbZKNLQSBNimeHGwGJNSqqWqwYXE6jekcTgrqT5bhRBTGMEYn9zTEZCAqyKh32JMhdRr4q5DXIcYD1rhQ4+rUjc8jYqxBmeV0O3pQgBpqNwp5sELyJEgfmBz/ojNkyfEzR7plHp/lRRX9KxMRVClW6yRe29gVivc1TUydOjQQ3uD6fbE7SXD9RVhH1PXwjGGWBEvn3DjfsLZ33qbW37g8o9/ggwGMxguPwrc2/RQO6rVCcPHNwxRGWJAo1BVBjURGYT24YC0cHV+RX3nV/DWd7n9P3ub6iVhc3HN8Pgc/eQZrM85OVoiC6E5u8/pW3uetI9pH6ZmPRuN3P/aS9Srhl/8T+8Sd8repURMZwNmSPwwSkCckugNkvfMOIgacNambHjr8I+eEp5dY73BVkrnlcooOId0gcoIdeUSW2PVQFUz6ECFpaVGjs9ozq/Y/ejnqYSxc2zvneK+9GX0/ov42hCsoXc1tl4iObbvJKKbPSZ0dLHFVj3aDpgYMZLKCFU09+vKNdchdXpzi5oQldAHalvR+YGqaXDWMbRDUtaLCmMtfjek3gYIPniiWIypiOrBGCoHpq4wNFgCxqyI2iF4JCq7/Tm7riN6i1oPpsfgsuWm02s3vtnZOMrv+PQ6J4s/FW3NQHspQSxOVinHFrlQ5JE58GaKSC5ty0BPJJcuT0Ci8JyIRsambybLJYloGJKCV5sUlcxkyegxTieJpEZfo1xDJwNMQIxkl7aOq1DCAlLkX0JiZYCTlwHGcsyDMO1cCWreA6OChqlDI2Pcf4xUjEplpqPmACGPZSozzHJybusmKzXlV4SAsWA1YjR1qEhAQMd7x5mxVWaY5hgPn92o1qYdg+ZqguKtEZjRWfHbfL4AGBiyDimLUZQkoCZbq3balUVZFyt6nGhGewf8A3kGOj9+hhy0kDBMCBjyhh7DApLuX5DcfOXG+xYo/jlKWEueQzmvbOQ8bpHZZpqHN2bjLS+ewIgsxkD8OKDZvGdzHy+VSIjmUYC54j/M0AVKNu5zdcXT/MkvwnN/OQAEs7UqoaDn7it5Huk5JGComVYzBKXtPYMXVKpE1FIF1NuUNSspPKRcEfyAOIe1BqKhch4Z0nMxLq27CqlVsLMYyRQaIbmg8RvU72nO1rj1XbrLZzgDmAGcxXc7FGEYBpyx6aUzlnYXCWpYvnobPbFsnz5h8/gSrjtMt2dhOiIDoVJcVMQr3e6SKg7EzQVmu8Pd/QOIEbfvUX+FCYoGGBjw2qNBCL2g0UI0uC6wk0j984/x915k8dLLnL11ydMffsQ2DIDDtwP9Rlm++Sqb73+A9IkyOSAMRjHOEjVSDZaNV+TG4v/4EV99c4e78yKX5xeEX7yPXj2mf3RDf/EhV/WSoy/f4fTV12ma1zh6Fnn27JyrNjKsLA/efpWLv/qQm188xVVCCIp6g1sL6sH7xN+gUSEIMaRwr3GCrRzOObwo0cFy03K+GRh6aFYV5rRBFha3rokOYmMZKoOxhqpuiBzh9gGO11Svfonq9Ijz/8M/Z9lvUYT9t7/G6ptfYbvxdFfPqCTtiSiOKIb6aAFLg2lqzHGD0ZooJ0jfo65DVKlMTQiKtQ4ztJiuSzF8YxBj6XPtf13XEFILaWMt3XaPcyv2mvIErBX6rkP7nqBdToPKyapGMBZMVaPWgotoNLiqYlCPsSlE0Pbn3D5a8OCs44NLj6dGJCTWRZsUSBITmavkkPBjplu1iIecA1yKzmTqbQ/Jc1CShGdyTufW8fiul5/TdUrZ46RosvEVQSSVxGkAbCY6QlICiQjqFfL6TrF9m7wXMYIO2eCSqQIrCbBxLHEMQZtRDUze4zK2POcDb20BKnH63RcBVxYw1/WPdp1k1RAPpGaya3KIpPRJKCAg5tLBfLCWB5J/0RwiEJ0lApKIhwQwIWJC8liWXDWjMeWQaMk/L1wB5baH8n4ca5j0zVTlkD8H/Dp6uFZ/zecLgAE/JZvFgt7KQy8uFwWxaVOPCK484PloZZrV3DqHtAIZzefUWsZYuOZz83GpP7uMCu/g+s/Hu8vPzyvA2Qab3rQZwJiDkvE6ASkpIeVSB+s9/3J8xWaf4rLPP39m/M9fq4wxn1pIQzI95ehhGQHP/HQdBUWJH0JxNc7Q73gNSp7TNKc4L3nR2RKmOHPnI7u2ox8CRpdgGlRS1zijPmfKDhgbiDESgwU1WCyqHcPgcyMZR5Qeaw1+8Ax9RHAEMyB2gakqou+5efoxi9OvEpY11XCUiGCCTw6d7J1YLBbokNyynoZFc4weHdEvAtcf/RJ/GbDRYGvB2BTnNbHD7K6JGw/BEyUSuj1m3+F3hjoKtrFoFbA9+LCHroM+UotNL3WIhJD2xaCK9IbWd1x/dE71Jcvq3m1O3niE+Qiut4K0l3S//hW3vvUqD6sj5OmOto+EOHufa0PslT6mpjJXjz0vXe25/dKS8PBDwicfpDwNC7owmIc7Pv3VlqtvXPPK77/OyVfuc/LOhkd7jzte09yu+av/17uEoCkk0UKwgm0jwYENyS1rvBA8OC+p7r4Hu66hSgmb9WC4+LNfcvbyffytW5ivvgh3TxmcS9Zr6eroI4b8n48442leegETO67/L/89fPxrqCvid7/N8tUvcfGrXyD9FvaePibCI20W4Na0z2pcU8Oiwh2vqZYNZt1g1w57vCZ6wbegXaAWR9y1uG4PyxoiqYWxgcot0ZhzCaKBIeKkwqtB6gpjI8SeuNvhgJCNATERWyliHKoGkRoxNa6ucHJKVSt9aAhiaeOOo9NTvv7aa2zju7S/7Pj0sk6uW5sDMFqS9Ex+rXPjL5kbHNkNLDK93jl5blKTMh1XzizehBJKncmxuUySYnAJ2QtkOJR6Bouk9ZPE+WDysWSQENTkJEJl7GgoJCOxuOOL9T+WHTommTzJ9SJeJMvFA8mYFm30IMxcB+NKUEr1sqDSYrwWkaup/DglSM7Eb6koSEIvW+zFMCr5aDO9IsniH8OvGU1INpSKXDVRMYXpNaakwWRW5nvkclIdz5+NM89LyZZ/sfUl5c1ojAdhaoOMaXNJiDAzbP/6zxcjHSrWeQlMkRDyuHgU0oU88TSP7JKSGcCbKctS9pEnn3DCLP7DnAhiAgWFwOEgQ7hce/Ra6Oz78utM+cPsDyWWVABA2cRQ2AO1tO4c5zt7YUXS5hjLBs20GWdoctr0Mt26QNLZREbUOR/qfCJFwY/Tnm/U5z7z2x94IeZ/zPcqL46EaR3m6zTmDgjk+uMuKFftjt3+BuNPoGnA9dmBYDFqEGuJYSAOHrtaEIe8pVQY4kAchKZeoKLEXlGxmGpNtV5hTlapdl16+utL2meXPH3n19x783WidcTVKW7nMMMNQT0VlmgBZwn7LfSC1kLvhP3jC6qd0BwtiH2H2Q2gAxoH6PepTJAEXtR4qIShi2jfoRqpKsdgIpUVYvaIxehJncYS810iS0rJaRYBjfjtllWMdFtFTo5pzi5o2kAcNux/+R63XnuBu3/wdd77P30f26ekok4MtYJvlWggikktfIOlV9BakO01fujpI7CFrospi36A8x9cst0LX/k7L3LnrZpfPQu4+wvwgae/uKZSl5sfmdRoqRPAIbUn7AxhGegVTEhdFwNC8/IajEVtg3+2R2/fIXzzBVgu6DQQ2w0SlE5DssQD+BhxtUMkdXBEHG5zhdML7NUe2wbit9/C3L3N7i//FBdvcGIS86StCNpgMiGNrQVZOqKLxBjY7Xc49dja5f8qzNphaksXArrZEjdXOI6IdQJzxlYpkTV4cJbGrhJUEQeVoV6SmrvEQGxbrDUELFLZ1JaZFB5z1QofLbYy2GpBIKQ2tQjDsAej3LvzMm95Rwierle6bsOzXbYCjcWY1Lo2jjHvbFCZHO+dK3c4EOyJ0nwCBaNNOXoik8w1+ccoaQ0LO2KRQ5IVagoRmMwtIWh28xsxmTQtgRcTFSMzMuUSljXFCIwzl3+2wGeCrICNg+Z0eRw6k9E6m8NorBR9MZ5X5N/cYMyg5kBqaraYi1xOa/u85T3JYh1zM0bvMGQG2pxYmZW7jsxHCRjZkk9V+g+UMEJ+zqYAlqxebB5vsYPLmMzoVS5l8pPRP82snKtj3sSYNFqmYsYn9dd+vlAL4+eD85p2WB5T2pTJzVViQUlZjlhv3Mh5VmTXGKTjTd4ExeVvBNGMJGG6bgEkh/A239POxloAyeygOUgo1wRm6frp3mNsfHb9PI2CSeboc36cFNbp55NBykUKyVCU6ZbzulctAKiAmefAS2lppaWnlYC6NH4psa8yBh1R+uGnICo9XA5lfKGnZJbJu5GQfsp+LS+598L5znO9v0kJQ65CqiUmeiIOxIMXKjXcPHnG0fGrUK/B95iww9UVftsT2h2DMRh3zOrWC9jjI1iCqkX6a+J2j+89IbRc/vKvaD/8kJe+9zaLk1OUxOkfdj55I/qB6ARbVwwhwNpR2YhTh1aKDz69uC6VUw1Dj/Q9MfQYIkRPjIGgPdKUeGCAhcM2ixQD3CreRJwVvCb3usl84H7wCfQHkL5i2PX43RbaPUKNrASs4reK5QnPfvAjbv3Hf8ijnz3m8k8/BiMMUelJVkUvyYKNWhGP1qytQTeXdPsbtjslBoP4iN9DvzO0g9JHaH++59fLR7z1H73CrccfYk8WPHt4xX4/5PyEnJmuEGJF1UYaImYlSHDJOtdAsAZnIieLMzQsCIOwb6D77qvo5gr77GNke0mQnmZR46oKk2PwlUoKEbiGeLRisCv6i5ZwsmL5vW8Q3ltjX7lL+9O/oNZrOmuxVDipGGLEDkM2wmLqLTDsoHZovQZXM/Q1g6uxdY3YHWZR46oaZyL17WPis7vo0ONWK4IdqNQy+J4ILJoTfDSoFQYfsGaFiTatertnv7+hEaFxjkFajKmxZoVKBTjECdVykV6TEIgYjE0ESd4o9cmKV+RFfOjZ9pHN7j3+4oMt276mKDhRkNxWuNT+j4lfc432mz4iWdLJJJckyc5kRU6KXUsScjYvVVOWe5K7CTCYoqwVVARfhF52k6dKCMmGYALBFpsqecq1YmBsdUz2YkaTZJ/JigsY/faaKxaKpJklDI4mj8ntlpUs50pC9eeItvHnmSVUEsRHVTYpTWaKF8phk7WWAEf2HBQFPYYYihxNYQHRBAQSz0DWcZqrCIrBnMem4zBzaHjm2S2JkqpxVoM3A4dZHxcRXkIMzwOc+LlVEZ/9fLEEwhJTHjViycYsqsKSqg5yfalYJmblWQ5BcdmUeEeJcY0duUzWVXlTmlJtIJMCe97zISbHeZRpK9iZQstfH8Tc5zCrZOtOG9EihVSKKVkjbUIlJ4+Mrvfimiqod4YODjZkOqaUphyg3RGPFFd8Om96cebznQBCQaYlYWS+wT4TNiiYQ+fXYRRK433GWNis6Uh6GHnZsosreroQebQZeHp1ge8ilTjULZC4ww4VRiLBVYiJDNd72v0OWwmOJTFuMXSordlTc3zvVRZ37yQOATvguw6/e0x7cU68avHbPX6zxVc9lxcfs/9XG177g7dZ3jsjnhxhK0O8ekaMA6Hb5daeC4b1mnDZ4WLEq1JXwqCRoAERTU1xVBnUE/uW4HuIPiUrW0ElwK7Fr5e4eknse+rFgtDepF4EIVvkQQmRBPS8oMHQdpE7TUO/32G7jqHfY3rD6tixedKllrF8hH3hV3z5n7zFD242bH7e0Zk2t5HVxBJnDJvQ8/Z3XqV//HEiydl5ducQG8H3grbCTasEFToxSGj58Ac9y9OKL/2dO/TvwvWnG/ZBESeEQdmL4xu3LGYb2LWBfg/WZ/dvDcYbvAddpz0/LA1dGBguLlm8+y7D1UPa3Q7rgVoYXPJkBAN20eCWa6JfII3HNDXLY8sA+CulW644+frX2P/ih5jLxwzLSGUapAKtlNrVqf7eQjQBHzq0q7G6giB43dFHZRBL7xaJMtg4TO2QdUW1XrN+40vI04dI3KHSMIRN6iC5OkmlnGaZ2lMvF5jFAk+Pc8L1Bx/jug6phUFD6mshNdatGaLJ2doeIznptaoSE1/nCfstoTZI5Vgc17zy0ovs2pZ9d8WmV/7ygw1eU2vmmHNvlMSNMMfp4/taxNXsHS6fSFYimpIAx0omKYZCsfwPwwOjeBIOFHsy50oOgZm1GSjSJSs8LVX0Ka6Y9LPMEgkz98CcmC5YNJpEVDEmPZZciRlHwkHuRB5AITrLChYzTzc8sAoZ5a4yU46j1pz+XtDEfLFLJt6EGkZlPxqZOSQiI0CI0zkx8QuYUVmn74NO/hajJJCVqYc15qTCA2N7/tDL0FICYskDsJqef5Dp+DkWKHbzb/P5rcGAZC5kxCfEpMndN1YVjFnoc8t7lhAiOm70gwmW3S0gMSNJFVRN2lSqs1CAjvearjX96XDOs4zVYk1nx8DkUp9tkvE6MgKImPufz2BmPjLOPAzm4GFNJYmzgc32akKYZeMVFKyzOFdGnpER/Mz36TS3WenI6AFJGcAHOQTTIh84Fw5+npef6PzZlUvPvssu8OI4ETwqgWe7gU8un3Gz2XLnzimYCnU11gUkKF5BnWF5q0YEnJXMVbUi+oA7uc363ovUx6cM0iHtNXq1I+yu8ZtrYjvQ9zsMUNcV4qAWZdhf896f/BvOXn2Nu1/5Mk1V41an+AixA/bXwArcEaI9XoS6FowVBEtUl0IWEgnBZLrbCh0s2iUij4gQQs+w37OOR7h6RRdvkgW6XKLdJgnOkFo/hwj0iqrFe0HFsThx9HtPjD0MnmHvGRRCZ4lXiukC2+//Fe7v/C7f/G9/jx/8H/89T3/eIzEyGBhQxBi+/MDw1S8JP/+XH/PKf3SKyIKLpxBPIl0r1DthOwS8mkR9LBCi8vN//5iX3nyZ6kXL09ZzSapZjsawJdDeb5Ch4ubXO46i0PUGvVCqo1y91VuaW0eYuy8yVCfI+Zbhpz9leP8hXQb2UiWXcgLGkXphsJVDa0e1WuEWS4gD/vyCen2MqR0x9nRuRdjscZuUYyBmT7Q9wSnWmJEFTyqLuhrMAt+s0PURslqxqBc0GAb1eN/iI4SNpbtp6FxHd7JkfbTiuO8wcUDcAmmWpN6QDlGHsxW+rjBHjlXj2Hz8Cf31JXVlGbTD2Apjj7FVk/kFLGKgso7gI9SOaG2ymaIShzZ5tHCwVNbHS7788sts9tdstnB90/HeuWesQjepvny0JecWYnmLRVJJmTC5hOfGxvi2fw7RTFaysRyu2fiSJE/i6MJ2Y9iALKsSHe40plF6qhLGwZHzH5L1H9GR0CjlfyU9kNzdLp+Q5N/k1p5GLQf/pzOdnITXAUlRSb4kGymaZVURg88TyB0ofx3B0qH3O+kzyUq+eGqERKFdwqVx3hZ9TDaMKacixoxHIqlyIY63iLNrzic89yYfWPijvE7eBc2VZjnDZEyGVDO3kg/qwP7GzxdoYRzSf4WBUM3MrTFZqePAixUuHiS5s1PtqZk/6Xx8UqqKRTKBUTrCcMCDXZQ6cJAtO3uulEQNjXmzy3S8cGAtHwJsmZZtHt7IyPVwbnNtX4DB5yC5MY4znT5hhDkQyWdO/p48J5PXvWzYQ/TzvDvokCdADv4yIuDnEdT44yykopND6gCnavIEjCEEAZOkHzuvfLK94cn1OafdCrOoULdEKg9+wJmIB7A9IoYQBzTz2q/vvMD67m08Sq8bwvYS17YM2xti1xL6nhBTG+IQI9SGqofgKlh66Dsu3v8Z7SefcOfLb3Ly+ksMsk4vzjCgrcXaBltbBm9xxtP3AyKCtcl6iiZnPmPRYHBNBXFAe4NGg/qBqvfUtiZWPnkHwh63XtNvr8DscDahfYkQY+qi1+8CRw9W2Cpg9oFhewNDSCWIWLyHfhfxneEsbtnYv6B5++u8/d/9Pj/957/ixz94SHcVYGX4/QcVv/P1Fe/9+TM2jzqG/Q45OubZPj2ntjP4XXKpdxZ8cGD6tNeuK7rzPdXJKX03cCmw0Eif5XR45PnGH93hV7dPePr9pyz8wGk01N5yfhm4/+IJr/7P/y7x7gP800t2P/5Ldj/6JAlho8hS6azSDIq2QKXUxxa3sph1hVRV6vzXe8RWRCJu7YmLM7oK6pcfsP/kU1abgX4hUMcU63QRkkc+Zf5Hj9rUWEqHQNztcae3sLdXWMA6wWnNzvdUMRMpec9wouxEWLdgzJI2KnXCb1gHsfHUJ0vW6yMuPv6Um/c/pKqzwjVL6npFtKvkFzSRSCCi1G7BEIW6XkBdE0KEGAjaEb1DbMUQDbYxrG+tefXFV3h6s+fN6xseXVzSByWQKLiNNRO3RokDj0pzivOXwqFiRRtJirwYZjrKDpPd9kxyzBQ5kA2K8U3P19dU9yOSgIOIycyBjBVgqpos0Wyz2NGNkcngsldJnE3yLybW0SRWBLFhvE6SN2YcT5ZsSYyOYIUR9IyeZCVTIifwqRFkVPDxOfEqMx3xnNGjBbwWI6zI7xzjU03cJWVkhTNgzAMoOqkAAZh7duc2mZBAQNAExKdQ82ze051STkjOQSpqy5nStljHMw40cAFLeSjxOc/CX/f5rcHAZIGWRMKJNaskpYwznkY2LWxxvxcg8LyCpSDHfHKOh6dPUfBpU4wupBKymCl8yUky47gNTKyJacOWcxO61EnjlT+XnIU5MJPnWAcLwizz+0zcYq7gZ/C5/D5X+gef56x4CmqdH5LXstxXcknlOMDZ+ubNkZDzPIdhfsGJ8lIzgUbJ5B3dY/nlMJlONWpILkmNiKQNftF2PL55xsvbO6yaE3ANsujR6DHdFhNBiFxfXXPn/i06P3B094Tm3n1a3SPtjjDscX5L7HuiGajWghUwXZeU5uCRtsX3PT4Icb/HdR21pFLAj//kPS7ef4kHb/0O9WJFOE3JTioBascqBmLrcVoxDDtqI0QrDARcY1B1aHT4vdLudrighCFgB9i+9zHrb38FdUtsu4bNJVEqtGmwtYMuYjLrl48QOsEGw+0vL2kvLsEYXD8Q/YC24PuAdA07lN0wsHtYc7bvqc5/yuL1U772+6e8+a2vsHu0Q55dIcHy/q7nxz/a8K37DVXvUyLk8ZqnT3e00TMgGLEMMeAlUEdADPfUMzzbsTpbsh6ELdBhGFS4j7K+imz/5II//Eevcf47d3nvzy8Ilze0GN74zou8+Q9f4eLDZ1SdEs4vePJvHmIaQ2gSZ0DlTUqS20C1MLg6yYzQd4QBaicEW0OzQGJAokc3HU010DvQ+19BTz/FX3xIVMW2glmChrw3naDOYpoGV68IwbDfe3wXUFPjjk+gahBvCXWgWVosC3YdRLNHokMri8R7RCtUoqh1sKyR+ojl+hg18PiX79B+9C7LyjCoYk2FaY4IpkI02/ECXegwVUVQEOuQymErhx9afGgZYgcObBwwvUMroV413D+7zxsP9jw6f8Svzlo+fLLFVNl1Hhj5Eab3s5QcZqNiNDA0y6VJRDATRaMDPYdgxzbKxd4RUyTgdBexWaLq5F7O50+SRcfrFKs3jrI0Uthck1jMyl6ycjOApmYQk1s9oJKNRNxUAjk3onTU/5Bd5ElxTyV6EwFPHtwYLjaTvCyDHmUjkN/X0dP7HLdKqaIa5e74u6Z5lNhuzF7T5DugMAxK8e6SFLhqOsOUcce5wVieh+ZHOeUXjEOOxQic9IRqKl80WnTULNthllT5N32+gGfAjxZjSu5z6V/shH6MGRVymsMMkSGIpCQKjJusa7HpAY9egFR/PmbUxuyuLoyDahFJ4EIz5eUUCiDF8ctCaUHFJXchYaVpdXOHMJNR9QhK5kClXDcr1Ry6mIzyssFmQGO++OVhqmSEmeeaFSk5Vj8dWjarTj+XecyV/PO5ADy/2Q8GMSLNEiIZHUiqJIphkyMDkrmOyv3zRtfk5kouMZ/idSFtQieWxgTELGn7lpv9hsYf4UJEO48ZIkMMSEjPWNuezV44uXcHt67wfk/cX2GDR/oNUVpwA9Lv2V5u8duBYeeJfkjxNR8QFSqJaFOlXRwjzhqqaqB7+D7vPXrEnVfe5OS114nHK+IguHqFaAeajM5GamLfpqTBmBg2g/doq2jbYsNA7EC8ITpS3PnTS8yLp0izRo+OGJ7uccbRR4+xJjuVkos09nD79RobHhNai4vCbhfx+0i4SbkFanpwjks8VQz0z5T1LlC/8xT/x48xNSgN9cJzuVjyy3c7JNSwNERTUYXA0Vdv8dMnO7woqOMNYI/lofFcBcMrWO43EVkocYicWseNdZje05jkqr6O0Dzpefd//CUvfvs+3/uDe1T2FroPdBc9f/W/+3f85P2Bf/K/+S94+utP2beeRSX4zqBDSlKKDmylRKNINNStJxCwTolLA+4UhhbnarQbCBKIV4p7cU13cgv35svEP32IE0UWAi5iKqACszDYZoGYRUqyqyyVa3BqUFvBoDRLy2CUShoQQzQ9dVXR92ucNdim4erZNYrH2RXSBJwaBn9N2/c4rfE3HXaxQqOhqhzR2NyEKfWFixrwGqirCh8MUlmqRY2tbUKAcY+PHZVZEFTR2KVSWuNSO+0Tx4u3znjpzm1ev/eUp5c11+qp1CUre1RgqQfAAY1KeZuLV0BJrmgpOVqa5e8YjEcghehMpqmFZHGTYv5oSgSMWWmkoo2sZIu8KMpchDjK4SS3TGZPFFMs91R6GFWTAZ2BnB0FowKWqDKGINK4s6GpgticGFiqKcTM5G3M0ejMcJirviQbdjFOMrfItvTvTK6rmTmbM/wpdMKzyoGxp0EuQdSQOlpNeQLpHKMBKfJRU7t6IeKyYh+Cx9g0WlM8HDGV2iK5siSWvJEsn/OeK0mHhhQKKKRTZSUle49C8QDlzaIFkFA6WP7Nny9QTZCtZfKDGMs7yhpPv4/PLW+o6ReTN2JxwWdFLs913cquLJ0rxuziKnZu2rnl7+XcYqHnJ22Kgi+bAwooSAg2Th6PAyBwCLdlnMfsi4Ka556J4i2AApsLPJvGENOj/cxx8884Fp67PofIdg4Y5icf/K6H5+ZDRuAx++Iwvjb7GMYXI9gIHiRmEg8XCb2yWAysFpGqEYxA2F+hoccOFh9rXLvCmwg+CdX12RGLoxU6bIntDUY78C0SWuLuhm7TMux7dEg5B7UBmiq9vFVyXcahJ1qLCynTPFgDHhYsCV3g6oN32Dz5iNuvfRM5/jJSBUxbgQzEyqeXxUdMiMRuIPYdfndNv9kjg0/85ibtMx/ANYGbv/oFJ3d+P1mKzYJquca3FxiTSietANEiPhKcR70wPDMMvRDw8FTozoX61gkn37lLeLDmbtMQhoFus+Xy4xs+/cklq4vA8UaJtSMulY+vlKv9DgnKORFtzjDA/nrga6+e8K+PP+ViE+kxfNfAInp+ri61lxbl1kvCye0G9srx4Hnh1WP+9GcXvGKEjUauBGqN3Fw79v/2GfWfP2Z5bIkoN+een/WWo2XDioF+19PtIfoAfVIGxhiqyrJ4kC2rGBgGwdQK4olOEFIjoBiGRBVsa6IJOFPhiHD/PrpwhKrHVoq1YJzJ0UWHSoWYCqkXVM2SyjowFnE1cdkgtqJyqXNlPwwYt6B2q/SqRsG4FeHiI7S/xDdL6tWSob/BnRxRHfewPGL18gO8v8twdUm32WKjUlVK1AAYNPpsLDcYa7BVhakMKpEYBrquo6oamvo2236D1w3EATMY1O6xdcXx6ZIXzl7kwe1H3Fqfc3MDViui7VM8OkMCEXKxoByIllHUKNmVLJMRVt5qKXIyvc6l5KxwDkAJO0ygIUQFN5N/Ok9oS2eZYjzPpEvCv9OxEjNgIObkQ5tURgYRPsYcLU4eXx0nm+ahOQcFMqGSZmBgUr+ISFbyhcFQIeVKZfAwlm3HAzGZDpQU6ixCUGCqnMoHKlPIQMOki2YMgRMQSJ01S56XlrLCvB4mJjpnUcVnxS35+kXMqwgxJ8Anm1ZngCYdNP913AezRzM3/6RsgBH8/IcGAzFz9EuybJNSnSlesdO/pijJMtIEJLT02RYYY0Rjkt606accRC07ebx3+jZv6OL+H+daXEIFbTK+INPKp+tN7qiComeu/hGNywzQzEEH07hMeQlnYx0BwOd8yq1m4YIpEei542ZjObzGDPnOwU5Z63mIYO5lmDlNpktpvk15GfK/JTww/ps8BOJtBlG5EjQqL6y2fPf1u/z977zN7339O6xWgb5/hL8aCNstfn+D2+f6bHPCq7/7ZRZnJ4TuGhluiP4qEw3dMFxtGPb7xDUQEgEKRrEms5zF9ELHKEkeRBJ5TMyxtQhWEpWvsRHCNZ/88E948dZd9PU1Q6cpxun3qfMeydOg/UDc7TBxoLFCHBxDLoPW2sAmsnCG7S//ksXLr7H+yj38bolZrqE7o7+4ZuevsJUluIAzUA2Wpx/ASeOpYo1ZCO214fQP3uDef/ldhkXFzcNPubm6gv0NVeN5/fdf4rXfOeXf/fcfcPM4EgZH27VghDtRuVHhfRc537a83O8YjOP23RMevHzGD37xNPmdauVLsebnHfyu6XntTuTO1yoWJ/f55YeX/Os/3fBP/9HLNCiXv7ri9Rg5wvEEz50hcnTLsTxuqKyhWVe8/PUTHvzyhptHG67fuaR5cMK1f4T14DTQiMEFRRaGPoLpA3YPaoW6sahRfOioqwavntAPNEERqcDVWR4PDOtjWC8xviM7qpIycA7jUr8LU62galDnEuWxtZiqStUnCL0PmNqxqGt8NARR3GqJbRZYu8bfbNGrT+H4iM1NhTqDXZ3Besnyzi0WZ7eojo6Q+jbN0S32j58hoQMJDASqKvUxiERMVaXcEucYBo9I8iytVmsWyzXS1+z6hsFviOEJDAvsYkmzqnlwdpf7J2fcu3XNo+uBSJvlj52SdkVyTLrkLmRlWd5UmXRocZMn7gFBsaPBZYq4mYnYJAdy8mLWnzKTcaXUbRJZmrLYZ7HqKBlgKNlaTh47QTCZslgwFP79EuZQKWmSkzJWI8kDOc9peN7oiYe0vOmCuWJMZ3JOzfjjeFxWvsn203HeYwn3Z1oy5/sWUDF6GUpL5wjqpxJCVUImjDKarf5iq5VrhZTjIOjoYUZjzgrTGQNhfp4ZwBmRRNamxWNQ4Fz5Pc9VEsAaQ0jPo4S/4fMFwgQlNp1OGxMHx5aVZgwTjMqzJPCNCneWEzCWn8yBxYRwyzMZH0hZ1fygVadTJvdI/lcnnoBJ6WfBnpHe/COjG2qGFgsalBm6Pjxrpljzcc8vetmQJZ6lMMauMiCYdHnZyGVjj3/4/GdxEP9nuv946uxcIXskZpcsYATN1kiRFjPlr8rYhKNk0AqJTAM4NZ7vvhr5L7/1D/lbb32HkxdeYL/es9u/S/f413SfPMFvesKVIr4idnu++rf/AfXZmoEOYosZOmQYaK97/G5H4y3B1Lg6oibz4HpH6HroWzSmcEEspT0k8hjjA7UmvvToe4IGBp+TvBph+OAjjl5/G19DjB7nFvTdNaYyRGsIPrU1DhpT1VNdIb4G+tTtzoAMA7Lb8eyf/384eu0fw53biHpkf82tuw8wwdBebukGnxp0WpAh8PiZsOh7YjS8+t++zQv/5DtcffIJT/+nP8M/OoewR9pAFGFvP2T5yh2++4/u8c//h084v2j5ili+vjL4IWB7Q6fwq6eBrz+6wLxqYSv8/a/d5QcfXfM728Br9yy3X+n5z3/acGSFB9+y3Hv1NjePOx7+8TV3n0V+/H99n//ld1a0zvCLDyI/3wrffGnFt99+mTvfeIBZN+ANehHo/Jb7by24++k1D//9u5z83qv405ph0yKe9E5KoI8B3YANmZ/TaSJJqhRbdUT2SBBcTAyUEctifYRGjzeOqlrSHx/Dk0toBLGJjllcjbgGcS55awQsicBIbHKvawwYUeqmITqH2uwDFEs0FWZxRvAR2ewwtSGgNM4SrEFDR9haNvGS3X7H4vSU5a1j7J0T6uMHdJ8+gU1LU4H3ITFlGrALg9SJY8IY8P2QvFlLpTMbjKlZrs6ovKPrrxhiSxwiUkWOjpU7x0tunTUcPey5HOIIdCUbS2Pcv9hCSfyPrvt5dn96pXWUtQUIiEnK2iiZdGhG6FaARTZmimIvkkXI4t2U2LWQXOZpQElvp4x2mxXRGL4YY93F4CnjS3rBFLIjzVUHKqAhVSOMjlPJ7nPBZK+wRh29y0nfpuDKoewssk1y2V4GNiKZMXAmGLNCHpvAFWCgRT6XvIB8/fHfAIVmPQOGosCLNiPGHOZJz8uU8cw9xRjI8rQ804PW1EV3MD1vmK5Znr/ORH7Jekh75/lctt/8+e3BgJ0p9JKgN7ecx//S8BKBxvNgICv/HBoYQcCcUYrygIWxTM/KpEjnGi+HAQ76YJfDyme01nUKGxWAkG6WkWpW/uX7MQu3bK6Zgi5Pbjwrj3MMW+QXtyDMKRmVAwQ7TqcAnvmBv+FTcghm9x83/4gsZJp3uZ5M/4yHx1Kyo9N1xhcpK9ocR5PsNnNGGaLnluv5By8d8U//4G2++sqXCas9++07DM8uCdtP8BfP0G2EPYmLW+HW/Vd54WuvE2uF/TPi9gLtd0nJdy0SY6IU3/sUC/UDQ98TOo8OSdGbDPJELM5mBkAErEV9cu1LVRH9gGsgtgOOwM35Q9aXb7E4doSuRqoVdjEQtgNYS71cJSAQOoJ26TFVFRoHDOBVGYY2xZA/fcij/8ef8uCf/CH+5ATZXSHdjpOzI0wYaK88vlOqU1CNqBpuOmH10m0e/MO3uPjoE57+yx+z+fHHDOeRTQ9+YTg9tty6N9B9eMPZ647f+Z3b/MWfXvLVvufW23fo399Qv7fja1j+2Xbg7K8Cv9cEqqNLvnz/Fv/dl04xD885ecNzfGp45T7Y2xXHy5rzX3g++v4n3DXKa7eFfhP46M9v+LkK/y4I//R3z3j7H7xEWNa8+85TntxsMVSc3T7i3q1j7JGlevF17p3WhLDn7usnfPiXLQsReo2IKLoHGxxilN5F3GAIpXKuDUTbEtuepjoGt0bqBeKKgLSYyuDqZVr7bHuItRjnUimfCMYkkCgSIBgCA1Qm5QRYQxcDUSyBxBDYDQ7TrKiOHP2Hj7DhhrbpQSwOS4w1MbQ4awmDgZ3gZc/eWhrrcKsj1q+9zP7TmnB+jsYBt66oFk0iPsryShTU91RiUO8T4ZADrKGu1qkcsnuKH/ZIHTHLwMlRw2rZsGi2MFjQgZSgbUcZNRLuZUWSRJJMTr8ib8ZwQYrXq6RS1CjglFSSiOQoYOFJUTSXUoswxp+L6DCa7LnStFxkKgMsMq7kCWj5Pa9H+U4zG225f1KWWW5qnBIjY5J/2aGQ9UYOzeQY/8jLNno4lTG5PI8v6evCbJhkVkoDyHJxlNFZJxTdMOYKTP8mCz0mOTjKyEI/HLKy1xwmYfSaJPbWOHpXJtE8VRmkAaeWxVZKyKAYdDI5cQtAOfjocz9N1vC4njOw9x88Z6Bk6WtpUFHKPmYbsgxq3KTPdyIcwUHBTjOPgILYvJkOkBOTsizu9NIHvGy/olAns/i5cU1jm8EsxjJFTE48LOfO55Ou+xn1PNeq6PiCTcGd+f1kBgp0dj7PnVP+b4YaP3PfcuJsRGMYZfziADOlr2L2u+ZnNCbHPAcc8nxG7uzxpclLHAKNKm/fXfO/+s4f8dJrjk7O0ZtL2t05/c0Nw07odz0MLcZHcBYzBN74zu8xrC223RCunyLtjkG71EOg2+H3ifBHvY5tdI21VE2FbRShys4nzasjxBjQWBODJ4TsMYgR1zli7zGLKvUI6Dp2n3xMfec1tFkQ/IBpFlQEeu/xbZ+y/ZsVRE/sU5vWQCRYCJWgHmTvkQqu/uwv0ai88F//Pvb+C2z3W0K3w506jnzFxnfsrg3SCHZvGZzwpf/mLa73W/j0gu2jawKptK/vDNsh8HADb2yFF17taC83vPbaXdyvNqxDw+nvfJ2b6kNq+4j/9NRxXCv//r2Wj37g+fsPOu6et/z+7zxg812LM4FVXfPkp4853ltueuX4lRXf+gdnUAth09H/7CkvDJ6jK8OD753xt/7TV3j6Scf/+H97h08e7lHfcwvLbSfcP6l548GKW9885cFb38QNHa892/Dur87pu4h6sGqwonQDLL0So9LtFeNgfWTy3ukRr1i7xIijahY4kzod0qSeB1Ydfak0y+9HEQcJWIWcu24TG6B1CfwZYeh7pK6wNmKqJcHXoAuqxRqA8PEjdAGmqrCmRlWwxqZbhYFFs6b1Ed9eE68ChBa9tcce36Z5cJcWYHNBVVd4HDE4xFkyczFhSB4k1S3EJdE71AVwjnpZE+Uuu/1jeolI07BaH3O0WrNYXsLNDmRBSpBL8uugIi6/01YMJturI/XsKAay9i6yFp14BUa5kZCWSFFW6f03B77PEjBIfx+fwUw6yfiICoOljORJCTSko2JMdfhiJq9BAn+RKJoqCUbjKiaiuZHcLoV9U7VznDzNOpNdReGPBs9M/o7KtxiTM+NolJPFEMve0VmZYCyVAyNTqyY5qsVbOlUQlFbPqoqaSNSI1ZSorNnjMxqNsxCAihAIOT/EjIDPZJA2jnT0tEyAaz4VlUISBWPCZdYn/39IIKzyWtpkkZeHoSmLU4slX0BDOW8eNhCmB1o8Amoo3bSnzP75RHX69/l4OJJdTDDSEENGlDrdf5pEGjswBtAL/Cr3K1mtwjRGYCrnm8FTmc7X0ra2HCFlkyZymwNkmZOEElKNY8ggxYwKK2OZQr6nliTHsmln4ysADdLuyxm2pad4QdgYndgEyyfO1mq2TFN4IIUMJGcOhah840j4x2++yauvH8PwEe1uoL96SrfbM+z2yYonNzSpLMH3nL76derXj/H7c4bNE+qhZ4jnDBtPdx0ZuhYrqdbfOMdiZaksmekrdUszuNQBUZOtElSwUoSEAWNTrXYIqDhMLcS2Bxmwpmf/8GPuvPElQuUxYhiokdjjFitEA4O2+H2HBsu+DzgS+ZVxiaVwkEQhG0PE+MDjf/4jhMCL/9XfZn33Vba75ApfLgPhdoePwvV1RW/g9G+/yMnv3+f63WukEtS3KIbOJkrjNsJGDL+4shwdR6rbHfWtG26/3HD64j3si69xVhluXm54+s45X/WBv/d3Df/vX+z53/4UXv9Vz53vf8w//l9/i1e+eotf/cufc+cbJyzOFphQsbz1Cu17F4RrpfIVJ6/VbK6vObZb/uhvv0p3seN/+Lcf8W/ev6HRmlOFWpUzD33X8ehiz7Onz7j+2WPu/8ff5OStE7738Jjv//AKa1Jr4JjBZhjAbAAqzDH0fUAaZbgWGhWG0HF8u4EqIK1ilg5TB6pB2Fxe4Wqb34Wk/IkDEhwyOBCoqyVSLfEI1gr0HWG/wxyfYRcLgjF4LME2rJZn+LUlPHlC/+gdTBWpbIV32RFfB9xynRoLhT22cdiqIviAbzvi5cAiDrij2yxevM3+oqbbtdROUGsQZ9BYEX1irUziyGBiJJgeqAjisAZWzRERT2RDXFiOTk64d1Jz+8zy0WPBG59aSGcjKmpMruUQck5EUgpFt2t2KRerc6rM0ny+IlEIZhRuGNHcKtckn5qJ2JTdl70GiVI7iuSuxYqo5aD6iORpSARkcVT8FGt/VFCZbU8kexmUGD0xJLUzdlsshpUkwJeqyUbrKYvdXOrtZ8ZUDhlN9OxZLqOUJkhTB0HIZQ4zGTcDBcUYzFVF4yqW5MJclSC5KyEaKYVrMbdMH7VDSPIo6DT+kt3vkNG2HW1DSb6SOIYh0lCs2LT/J+kOmEQfnav4Rg9M1Lz3EgNkJHlgREiVIr/F57cHAzJlnyqSlGouUdEEa5gU9sziHEseiqdgFhaYcxOUVZuHwg/cIxNVZSHimOvmspkMmlFSykKUPPaJbChvXM1x9/LdgTdgrox1usmYzzBG80ZQUsY6H06ZgxaFf4BLpodefp8opGcAZH6t+QkyBypl/ef3Lf9XXuC8/aSMc4ZUy1zHt3jWRavMISRXaKOet05P+coL9xjCI7r2KbuLHf12R+wGJESsMRhjEhgwAvGUl77xNTDXmP05Q3fOdtey3+0IfcRFx3ptk2A3yb2sBEJQnDGEQbHGIjZZF5IBaQrdDRC7lC0eHRoDYupEpBNa4hIWberO1+6vuPzofU6+8QZUIREm2YZ+6NDlmmW0eLV0HQzthhpL7ASNgqkgdB2mMfgOwj6V5H38z36GxXH7v/g2xy9/ha1tuPHv4CrLUQOcLLn9ygm3vvEK28c9bv+UzeMnWOnYi6LW0hFoEXod+EQ8L13C8cYgfUXQlnDnDLmj/PTffML7//YD5KbDCexfs3znfsNfyY7bQak74V/873+Iu7Xi0/Md/4u371C9cAczLDCuYXH6Mi72aGPx588wz1pO36zxjaLnl5w83tAGZUPPWi0biTzCUWuPC46TaNkPWx7/2Y954e++xVfefoXt1ZZH7wTWCGuJrI8i9YnjyblncRzwO8UETcq8VnABqWqicxgvqEvdKl2zQLc3LHUg4vBdxIhiNdH1Gompda4Db3zKN8FA54nOYs5S8uGgAQ1LRBJTpNxTTGjZ//Rn0O+IzmO9xbkq0QgPniA73HJNtVoQnCE4QYwmABwj7WbLEITFiXJ0Z0VnFQawLhIGQcxA3/cpNOXWECzYzPGfm0CldpAV6/oI6T3aRE6OV7xy+4xXbl3wzmLHeReoxU7Z5nNjvViS01s9Vv7MbaQiOwoLjJnnX6kyemtnrul5nbsWsVOM7nytoljLHUZxikz3HuPhZRQTP0GRJ+n6cczTUonEA7KkOKmG0vDOCKhnEnKSFXsxkNIJWi6imo9nknHMgEBZwTivPsh0+4VxEEZDKCcnZHgQx3uU7oNTmKHMb3pKs4jA9ChHpc34rCNT+t1o7ZfwxExNRjQnEk7iulj+RT/G/FzJxuVz2WW/8fPbkw6VmRqYkgUNKVNKp/w5k6ypESZ9LhjITqlR/6bdJzpT2gUtlonliWpevKz5031L4x+Z3Cojx3VBW/PuhuNmLyUwwtTZyUz3Pfg5n1o225j0wrg5DhSsFoULo8tr7sKa8/2P1v1nV338CNO66PhFvtHsukxrdXCN2QYdV6K412ZzGDc/wugdYPr7ygy8dLQEt2Nz8xHd1Zaw84QYkJjdrjYlAVmTmp82q2MWdxzt9WOGJ5+w71usj1Smom4EoxZjBlQVIy7VldskrAVhsVxQmyQYYkxtkEUSW5uagPoqxZAlImKTa9LE1I0vpiQzMRYnkatf/pyzl1/ENzXiB8xiiQ0DQxsRqZD1MbUYjqzC1Tm2qfB9xHtFHQxrxYrQD5FBlXY78P7/88fIkeXe3/s6R6+8wXK1prt7hcEiZxUXP37I7uE1J68vuPr4IbtH5wiGIMLeB1qF1li2BC6N5Z0tfKNTzLYlPG2RtfKrn/+SH/2rDzjbdhzJgqXssfvIpx90vOYtSxOoEVwXCI92PI3KRx9ccGup1G+8RTy5QzyuiTYi6mkWDd2xIPEK3+/oNztuhcjbxvJjVT6SQC+gwXNsUjazfRZZHCmLoeXmnU9Z/e43eOHl96gvBnaXnmVUVidw9Jrh0TPH/qmniTkH1BoEjywty+MTYgw00RCbBdGdYWxD/OCnuL5lcfsug4sQevp+i7HJ4tUMUk0XYDDIwqGLGlkfoctjIjVIQ4wLpL4Ft1ZUdUX753+KXn9CdWRRt4AoRJcUSiUVQZXQblHfQ9NglguM6ZPFP1Qp58Up/WaPX1mas4r2okM9uMrS93tCuwcfQDzRp4SH1KfHg/rEwyA9lVFqV1Gp5/So4v76Fi+dPOLBmeXiY9BSt6/TG46kngOaNfUE54tplOP8RYuOpGwzmSdFFiX5GosRN1rAswotcvggyxbN3t9yelZzORyQ7lMa8UwmlZRbpTvoJC6JHmsdiBBIc4tFDmeFK6o569EmGX5gtOmk2DO/wRSWnRlAM2OylKNPBk5R9CWrf96KOMvAUMABFEAwzwVIoCiXVEr+WUuuxMxjM3saz3MAKVPVRpG6xZQdCZ5gio6UpzgDF3NpLyZ5YUZa+rRI/DafL1BNwJS9b5gpaJvB1Wxo8wS7EQhQpsVBeYRMz9GU/WCSEtaMOucxtPKRfD0tyjBfshBVTIku+tw5c9rhfMPiyZiOnJ7A/IUa4XD5+2zDjd4OZhugbO40RhnBw3SNxHP9OWtd1uIAFD3/INKL89lzZyBh3DUjOmOiPZ4AQsmglfIixOkYyUk0osrKKGdHsIuPYbhOc3KWSpNSr8QiNoVvbHbLLVZHXFw8pbv6lGM95vhkReVvwArWOKw6XLUCTWRUViT1nQ8heSSCp223WAIhKt4nL4U1Jo3LCM6adP4gBAY0BqJJJCkx9rhBUd0R2is+/f4PeeF7v4cuV8StTw1q8LQ6IFulWixp4ik69ET1DCZSG0VbT9cZOm/wd2ua1RHHVCwVnv30XVYPbnF8u8HjaI5vE4eWH/3JR3z4Lz7k9b/1Ne4uFly//5Td9cDuRrjepXj+ToWtBl6XJX8onl8b6DaGfrOlFyW6G975t09ZtC21tUT23P6m0PuIeSfyqhVcDFQq1KdL4n342Uc9F/aU4/u3uKkjizeWrOoFgWN8PxBXj5GnG/oPz5HeEezArdrx1X3klkYeSuJ0uEOkU8MVkaZXjs8hnBrC9RaMY3V2h418iBdYrixqLHa5Itgbwt4S9wPUBg0pobNaH4FYmvx+++NT/ElFdX5B96e/wOiAdh1Rjqnuv8p6XbHbXhGDx4pBA3hnaNZHyKoBZ7FugYSKQZf45QmrozPiC0uiVzY//DHh1z/D2JYoUKkF51BrcCqJqImIRIHg0Z7Ecrlw1MsF1sHglb73NKcecRFf3cUsDdIGAvx/afuvZ0uSPL8T+/zcPcQRV6fOyqosXa2mdU/3SExjZ2YxhOByCWHY5ZrRwBc+8IGPtH3gf0GjGWkkdwEYVwCDBQyYxWJ0T8/0tO5pVboqs7JS3Lw3rzwqhLvzwd0j4tyqnqkxA8Ms855zQrgI99/v+9M0bUMzm+HbGl06kDGSlSE7oRfEF4GsWQ+ZRvKSom4pS8flnStc2njA7gRyPA1BK9Fr9qM86lzvRT+gRZ28Fbd88n0K5aLBW0909e98BIKzYf9s65NTH11KkcCQghEiGXE7v6hEdSJdGMpsiRy5joEPyNaQfuJJkV0i0TQRowL6qIjEpBOv8B0YiQNCrKdLWZ8sBF2jPcfs+ERHSn1kxLYX8IYgIpHG6JDYaWkiTQyuBBGUKN8xf580x4PmhpzFxXaE5BfQ9W6NX3VBEfFzkj/TtSFyY/Dg5CwYb1aiaKMvgvxHBwOJ+SfXSVLHI6Pv/AJ6xhxvZM2Z0Ecrl0QQ0c0wwSs8xmF6XH9+6KzYTV5kWjJsP/an05/QO8114SqDa+OzZK361YW/nW9EGrcajH9wtfSf0zvtEldELYCP4ycSCZ8WW2qu1/us92P40jsGPhz34PJB13qskghImpABgPC+Dxv0AUwFADZQm2Gjg4yjQjGv5zifMyo2UdLitSZU+BOUUgNnIYe3imxzhGLMpa3LjPKWVdOQsx0tHcHZprZNUNs7i402NGxIf9ysKrQIzmu00ZhMhxTF3mNjMRCFonUtrW/BbIIvca1FjwXdOuq6Qs7P0VnL8vCMs0cHbL10E2kr/LxCZTljJlQC9dEZWuW4cgNaGDUllatwVyYUG7vsXH+KbGcTH7yDsGhK3zKvoS5C3v3m3mMe/PG7nL55Qu4Ui9mMcmuCHpc0h57T05Yns4ZlDTPgBNjXK0yr+PldwWU1q1nJ5KZl9uYDvvTCJm/UOyzvLBlZz6XPXeHsrQVTpjTHT5BjR54XjD63y32zYnXvkN29EW57g/b4iPd//4yz41Oe+spT7H7sKot3TpjdvcPs4JStjR3EK67dFs7e8IxmwjVnUWjGwFvacGgrNpXitHLkK8jqJavFGarIOK81tW+pJ4bpuMVKg3UO5Ty+0kFVXrbkY0NmRghCY4V8bw833cQoy+KNd+HmHubZV2BrG52PWBlNbRRZbkihrk5ZFJ6msrCqUUZjyxGMphSTLSbb23jvsfunLF//IfbRG5jM0BoVzFde0dpgJ1ei0AQzkE/OXELwQ6lb6maBlRXZuCAb5SFs0LbgM8rNLepWUy/PaM5WLI6OyLQKpg/XkkuFUeOQTc8sca5A6TFODJIJo2xM00BZekblmLLIMKrG+hiOljRyyl+gV0MaloBAYkCdB0Fg1E6H9LVRZgukxoewTDwpk1149AWhyafIgiTpJkfC0FaS9gN5D5qjgZ0zEZfOe1+UD8pXFeYZRyhqJQpvYxRB8jXrGHloQLyPURUp8UfkEylKYGhP6Yl8L/AkVXsSbgi0SbrsSVGrm7huus9FIanjDtI1kUBYJ1PRs5ounJDgLzE0r9gIFAw9IFnvd3gznSA8gBMDvXf3W3hf0t/rYxlj0nyupfP7S4+/nmZg6Aege0DQLRkZXhM7nWJjVDofkZ5AzNHYD0zBUNVPXJQdQ5Z4TSf5DiZlyD+ddMhMlOr9AJMqKgXTxvyO3asaAgqEFD/djyX1ifWN2Z0ebNi0sNLhBy89+REMVVvphSbQNVR9ycXGLtxzoR9933x/bUQe4h2eHm2nwYgIuBgri42bODjOSEydKd6jsRgzJstGZKbC2ByXW1oEg6BFDwiVRqTAbBXkGx6zbGkbS6ELBB3pnEUroW0tbUooJG1IBNQ24ZXnGaIMmQolYp1ziGuDc2GMWXatxzPFjMbBM90ImfJI3VDPFmTa4FWGb2qkaJg/2Ge0N6XYHuPrCms9LlNo5zFbsDg7QbkMm+/hb20y2t3ATwpEKZb1iubsCHd0TH10DHWDNAqvDWpjg8n1LfQLezx9eYNL37nLyZ+/g5+v0LZg/PxTHNx5NSSG8YqFs8xFWOA5dPBthJtO8fSOo55blHE0d+aQ13zik9dZPr9Hrjy7tzep7zykfGELY66zeu8J5CPGz+9w8OO3uDyd8uJnxhy8+z4nPz1kPne0p4qjbz/m2heucvuzT1NMDH/0Y/jiFcd0Oqa8bnnWKQ7eqKkqgZht74lrqBScWphqjVWedtWStYpzMg5nLbkXzp94sgwmRUabb7Cqz8mnQr4jFBvCKC/RDVBa9NYVuHQVnZWs3n8PP3LI3lXmRmNmZ6xkjlYarzVOaVReQJaB8YjJMdmYbJyhlZAXBcYUNPOK+t7rVKeHNEf3MW6JKgyt8hiV0aqollYSaI8JyYq0D0zJ6shUnQOVBU2lczTzGrt0OJOh8gUjW0NWI+UeHI04vfc6tlnitcG1FuUzsCV+Zck3Na4MoYROZ6g2ZFy0ZkxeblNkLWCxOvirKN8QLdEfJOKJOcqAzKyZBULxq8CQQiSMdM6IgWmoKFE6R3C+Fhg6HHkVw9o6P6uBrb+T0FOB46R295EcxxTDUfBRnQOcj+WNUyijRrxDqaBrcD4wzFZiaDCxGqskP4nUAel90wb+CSTek+aHkIQMF9T2Xb/ic4ZRUmleA8jzAyASqxFK2Kc+JhtKVeu7hEyRVaTaRf3Jnpckhq4TAFtzIIh0ufPlIAZ+DcJIO8IehbnUzoDo9+6LCZn4UM8jCngf5fjIYEApwauA4ELcfxsHnoXBiY82/BS7Kb05IaYx7pCV2HhdHGTMTNiF9617ztBnNBww/vS5Y3zJtq07kNGj3YEUnZLpqCHSTn0cSs1hTCl/d3Ca9HSJLbouBcY93Ly+68/6OIbIsvtdqYg+6QEPLurrPozZy88GAmnT+BQPHLDoEL34gfq/720ggF0azORLEAMrIOI/53g6U7xy5Wm2dw11Y8FplGkp2hZPDUrQaMQKXlu8GpHrMUaykB3QaNrGk9kVdSPg6+Bh7C15nlNkOtiqtcJMJpgIHLwXxDZYAWUbpNU4azCo4GA83mS0dSnYgv0CP5uxPF6wOp1Rn53iFnNcU3Vpr4xkHPyw4unPfR4m23j/BD+ryHODtRPywrLc3UJvbyCbhua8xp6t8ItD/HyOP9rHPjnCrSrc0sOZ0J7BslLsm4L85i57n7nK1i/fIrtmOPzzRyyfzLj28vPs/+Au2WJBtvD4BhqnsVhW4nhXFO8vLS9vZTw4FV77qWVRe04LYfrWI9qx8NnNKbf3F6iFZXpzA7lxk2w6wU9y2NU8uXPEyy9eZ7yhOW5nbFxX6HOPFILzDWd/eo9H7ZKt27u892hFNXf83V/fYvP6NqPdEr37iJPXK9QTeLhSPCse5TS5GHLjUb4l2xRyk6PLIjjyuZaFc/iHYD424pVfHXPvP5yiCyEj/FOuQG/ukt14jnbnCoxymrs/on3jDZxrwrr3NmQNzDTejBFlUMrgdY7LR/hihC9LVjrDTCZk4wl102JkgT2d09y9hzl5jyxvaScT8AqjYwIjn5iZIFrjVNAuiQJlJOQyAKKxn8Y5MpOjfMio59E4LIszmLenbF6bcjY7pT0/w2mHaxqaxpO1Oe1ixSpfUtYNxeYmmd9AjVc4k4HVKLEoo9neu8rOdA+pbGTgEvxhcFGCtYiYoPb3gYokYuPTxiQo9WPme/CC0uF7cAOSTvaCYBboEsgNCJeSGLKYdAai8F53WgEBlLhOvR1y0aqBh77vndech9ZFn/CYklcCk9Z4UDqENysX241jcz7kSOiYYKKjkVl2fM3SZyvsaVSguDakRE5ULwGBjtxF+pYARIqo6jShhDlN9QoG+WOSo2C8JNClrk8pZDCc7OCAhNoJrWtjaGiS8sP8KQk+E0kXE9ifjZUo18PaRUx8XdGU7hOQkO6ZQ1zQaaA/wvGRwYCLg++ke9Exl1BMz7jmMzBgi0NjS3IUlMScoodonIgIJy8wqt72FGejl5qH5ocO0Q0k7os2/+43P7hn0FT3o+/a6VHchRl1UYromk4vc/iYwQKEzmb3gecNtQJrnYnz9AEkeOHoNB7SjSltgg5opLnpT/YbI2XUSgvYQ8rJnRB4sqttF5pLm7uMx5bC1dTtDFcX6FKj1Rhra/AVmBEmn5IVeyg1YlJuU3mLrJYUheDajDLzOJfRtjVFWeKtpa4qlGTkeRaabzyiGrxzIZTQ2piKOMNjWSlNtnudYjwCX2MXZ8wPDpg/PMGez1BNg7I1xtcBwHqPq+vgh3B0n7ePH/DML/wiZnMTFNjHT8AK3hhkZxtB0x4c4esVWVvD8oz27AjsCooG1QitVlQZUBryVqgPK07ev8/+dx6y8eyYZ798na0XRjx+7Q5P/+1Pc/VjNzk5epdJ4ylxmJXFW8hcRiaga4vaucTkcknz9rtsGM83ZhVPe8dkZnj94T7lgebmV56hLrYYlxu0u4Zsb5OTn/4Av98yesVR7pRsPneVxeFDJk7IWs/qoCWfG/SuZXEl47ntkjfvnfLP/43ls58suLaxZNS2uG3P2bxgs2i5MsoQXTHeaRjtChvbEyZPPYWajsn8E8baMUXRTqBoFAc/PuRL/8df4NpzU6r359TGUUxz9NYW+dVbOOfwpuLkD7/P6bdeR7IVygjKeHQGkoHkxOgDQbRCaYMpJ7h8jNcFmBI33qQeT6nHG+STCdlkjPn0K8jxVez776HqE3LTUjcOo0ryvACdhe2LBMKsdUxUZRDRkRjr6GDnkSxDORfMCaKDKTPLaHBU8ydMt0ua0TikXHYVVVPhViuUapC6pqkWNPU5Zb1H3lTIaITKx6iiRZcbZJMRn7r1cb6+8ydk4mm9pkuak0qje9/1GZLdeT1SoC8oRGeedIOQb7lAY7poIemZjRv6IXSEItK0KEglGTQ04UL+OIKWwCWWG+WQkKY3Pife5PF4GzQLTkJ6Yy9RDlYxQkz5qBkY5H9JvAPpkjCRwNHQxznO1JCed7SwI6EDYScJQGv0lO631F4nSHbtpIRCvnt059Xvu1yRJM1Kel4CC13OhaT5GDbv6VT8H+yWB1Idgx40hURTgScNo04cPTv8q46/RgbCyCAlDdLGhZZEx/BfaDhGGiS1Rnp5STr10HnYDxjY4CR9PP0QUMSGupcz/D31ITE3Gf7IhYvCp44ve7rcBMOFIdEAIoP7Evf/MLQ1RKDdbxfOD+Nc/zLItrZ4hw0Pz6fdP4CCQybv7Pr1QxCSQEByXOhMFz4QI2wf4uTBO4fzLbnOMErIdA5iKIuMJsrb4nOM0ThdkI22yfKMxfkJ7qiFp/bIJptkFprqFHFB05SVGbnOYtpuoVQKaR11VQdVmXi8tYh1MXogxOlWrUGKXcorl9GFYKsV88ePWRwcY2cLqBaYHNAK1SpoCM6ILozNU2OkxR2d8d6//112P/ZzbL30NPrGU7TnZ9RVy8q2yPkcoywj01CfHeOXc5S12MaH2HohxP0KWNtQrTxt5VE2xG4/fmPO4YN3ee4Lu+xlxzQHK6587mM8fOeI+vSEnYnnDMEsQra4LTyXN4NC8dr2mBdKxXPPGJp9x3uHnpFvqZWwXxieKUrM2GBHY7Jre1T1OYvDR2yUGct7J5w+2mD78iUmuyOqRcXi3gGyVbP78ib+0hS/tOyM4CmtOXyy4g//rOI3Xi55+mNTJpdatp621DOFqxtUmSETxWhrSjnZJtu6ijMFzgljr5h4yLahuGygHvPwj+5x7cvPsfGJyzhl8LpE7eygyJifn3Py59/nyZ+8iSksJvMxVS6IFpQBpxUKF5WLHhGLtxXKG1LpbLtsQ/jock51XtAWI/zGFnq0SfHMy7QH9/CLAxSrkIwKh2gfBBkErTUuFkNCh72uiRXwtI5KTYPyHkTRise7jMZaDBkaj89BTzKUb1C2QGca7xxt24CzuNoyP2pplpZyuSLfnKKnNZkPY9TacuvGZT7/1Kf5+tYfc/dYYnGffkcHPup7vC8qaBFai4iOqXqHV3eU9oP0leAIKPjOR3iYwdX74MCbfhJJzH/w3OSjEMWfzi96QO56h/LQiOttDSAuWo9jlc/EHZzrnMd7GiVRIxtzpXSakRRdMaDLHX2LnV/jDwP61wlAA3rYHdJdm+alF9GE9Rhud+G+OLx07wDIpKaHZaOTk6VEBDXM7vhhkmrkpgEIxPEr0bEnvutBClVM6bn+4ycdUoMXJbFinajwiIQ8k71fLF3YnqTilUIvNyegMBz8gNl38fADz/aELnv1BH3o3yBEMGkd/HAxqA+VuocxnHzArhIX/zCz4fBFdYtwyNxZ/5w2wFooYbr+Q17QGhBJcx0f2E3PYBy+7+f60NZBTR9UlJj/cI7StfFcdAQSifY27yOrDxUel7VlvlqyyxjvMvJiA5Eltg5T71ROVm5iioxq/pD54QFyPqF54VnUlsKPSnK9wK7C5nY+ZB1LgpByHqU9mUg0Q/jgRe6BtsXhqW2GnlxidPMqomr8+Zzz/ccsTmeU4lHjjEaV+LbC04ahmahKFYeIQbeK1s5BC+3ygId/9kcc/OgyNz7zGZq9Esqc7KQiy0Hj8PMGISMbjXAoGuVQy4KmqqhVyLhnPcF5UoWiTB6DMY6jecsPvnnKz3/WUr5xh8s//0le+urnOXvwLYr9U6ZScILllrd8aQtuvShs7O2wfyToCrzz/OZnd/npj845qhrycsTi8Jx33zzi+dGEnS/cxjxr4G3P1vM7XJ+1rI7OOXr1gCfNEctlw+Syxt9fkk9KVhsKVMvs/VOmpuLqDth9xfWrBZevF8g8Y/r8JSZesLalXSwQrWl0wWi6Q74xISs20ZnGzStGzpEpmCw8udlg/PILuNJQHVS0bYvJM/KdnNWTB6yWc5y3HH/jDnokqHFIsqMVeO1D7twMTO6RTKHzkNgHBKc0YhRSFKAyyrzAaYNVDktNu2jQdYvLZhzlI/LxLoWCcXMCEooN+VRqOjeQ6ZAKViu8Ch7YiWYo0YH6RBAqwxxhjQNlaGtBlwVSlNBYMi0YUXjrMdbg8azqBrxQLc5p2hlFlZNXO7i6IaNB5RusyprPPvMSz+9+n/snM+ooLboknV6Q5jtbdmTajmTXT7kDkiNeHzUw4Ggkv/MUHU0HNOQDse7pSy9Ue5KvVcgflxwMYzs+aggumE6HpZg7231H28J4VHx2p4kcElVRdFI2EMLXP4x5Rp+K1M8hTWTQ3lBwinPsI5/5gAx28fgQYbDDJT7+lfUxiPiQG8Wvz2cqPqQGGobEF7tIcaTr/xpjj/4d6WlOICP6fPgQITJkS3/V8dcAAw0iJjBHFW3+fc5QOu/+OJY+qU9GV/oYQMzaCyDlFkg3Js1CQkPCugNEAgTdzF1AUImxoQan/Po1HaKTDzDOQSM/++jiPiKT7Zj+8J/vQ10ubrC/Um+T0O+wP0MQkU6qtb53EkSvL/vgwo+oODj/uFCKmKGGIIYLOcA5BIuKnrfi4GBZ82Q+4/KqQOcjnFmCNWR5TVuDLooQ2z0/ZHl4ilsIyydPOH3vkJ3P36Q1JblYRNfIoqWqFpgshBTlyoC3OLfCW0+mNdpIkOpEyHRBVSnyzV3ya1fR0lKdnXL24Ak0wvZGiXdLlmezmGktzJFXGqNKUAbXVDhb07oG27YU1tLInLpoafeP+Ml/9z43vvoFJp+4icciOjpcGofJFGQGKx5pI7SNJjElYKJjmqjg/2K9Zd7ARGmkrjj+qbBZvMkTnXH1yy/w1f/zr3Pv3/8J+28cMZ87phuGKzc14609pree4fW775A3nvZBy/ZXn+FXXracPTxgWZ3x7vdynrx6wMblkkn1Nnf/1Qn3/+Q+q4MTzq3n+dubjDYyjt45ZP/dBc9fu83tX77J7J0z2nmOXYE+PWCcefJLcHwGL7xyhY1nt+H1fcyqIL85xWc5VBZbtaFKYDFCjabURYERxdmP9ykyz+b1nNHmDkrtoOsRxdO3kJ0p7WoBLmMxO2Hxk3t4tWB+es58uWRD51Q+eO+HwjV0yd7EhrLBEuuSqCwnyzeQfIwzGVYUVukI8AymKNBoWqcwyjOyDStRqK1N2hNLER3nlGR4Z/DW4JSEpFbWx/QnISRWlMZ1XvAxQkbCb0o0Ra5pJyV6XGLMBluXnqFZnVJVJ/imQRuPWIv1niwzONvg/Rhsy+q8oVo+YXl+xmS5y3jvKs30CpeuXubF3Ut86/0T2jrr4LtcEGI6JX6S1CND9Yl7DPIB9FRGIklJ0vyAqSTVNglkrFOiTgU+ONE5i/uu5Q4IQN8FlRi8B5s0P15CVEASFh14SX4KEjiasnEoeuAWkIS3gS/BB+ho3O9+0IkEGIZh4Gs8INzX8aABvV77rROeuumkc7KMk+FTF4bf4yR6fFD9S1jjKmoFXOSisVTSWhek44M9M0jO+g4I+R+GpL4HSxJBhEsC3kc4/hq1CdouxM+rNk62Qaj7YXcC8wWPSRlK8SlkLWoHumDMJO0PJzQ5Pwyl3x4BdlLzUMsgw3Ppng/hyBcR5RrKZHDtQCvgU78H5y/ijLTO4mdxQSuwvrAkce6LDfIB2LnWz9T2h8eN+uGCJTxf0iZKJoH4twNYFzUFERAEZ14f1Oregg0S9kHV8ODoIS/b26hshbUVVBlWGlw2Js9y2uWM5vSYdl6xmq2o3IqHr7/K5U/dolZCm00Re45XLeV4RLVaIgi1dUEB5QTB4G1MZkTQKzk8rhwxurqLlyXL0zPm+yeYzKCKmrqa08wWffnUrEVlJpgkmiZsHiUYA9ZZzDinaUJ61LJyVHst2i04efVtNj75DDovKNoVbStoKbFli1tZlK2pAUuDs56mctQ12EqoqpamCfEaKEPphDPleOnGiOWhZXlyzv0/fYuaba5/+TLP/+O/y4137+OfHINfoU2JvnGVo2XFo2+/z2XxmHGJry2rF3Z59ffeZ6M5QdcZO9sZ7fk+d/8fxxzeWXDSWh5bx9ZGga2F3edegYOfMFMVZZZT3nwR6x+RecNicYJuJqxOFry/UFx5apMXP/ccugH9guHsvWNyW7L3iR3sJJh/rPKhaFCr0SvLe//h+7R352xdGlFe3cTsXiPf3sFMt9EyolUboAzeCO3TmsnuDjy+wzv//dfJSqHVDVKD5BLfb1zaCloENfXoPEOKEboY402JNRo9ytEmB12AKvAmw+nABDIMVhRSWcrCoDfG2EWFOztBlRLs7l7hbI1qBWdMzG4ZiZcyWC1oo/AKnIqOeE6hW0GJwVqFyzLGOzv4hcGMNcXWZbLFBovTJ7hqTmZM0IV6h28dXkJKcudqbNvQzhTn7ROqdkFZrxBzi53JDrnuQ9GIjmeJDAU7tV+jHd6DUqrz1Pc9JQB8cNLTQ9p4kWgEwCGRlirpS+CG0+G3jjINfMO6z0L03CdZhUnwwSe6GbtgvY+aRxcrIgZzoUMQMV1EUyoNn1ToySy8TjEv0E+JAuha8rXhfCVe03F7OoFwbUouaAc6P7VBs0mbwiD1/mD2A+UN52J8RKSycS6iUOej82BKJYxPToh9PphesRMBYKSIAUjEaBF81BL1PUkc9T+6mcCrNtp3fIdNZDg7wZgVOxEZlwi9S/oHvRpD0aP0YtLLUwOEK4RyxIOX0c3MADx0YOTC94SW4YPXrnUkPSs5niStRkhG4onDiMkKui13EWMMF0xceGkJfAB0+At97iZnoF1Jkv8HQgv7DTlo/INDE4kJxXwPVDpIHxCjx66BhE40i5oB72MufgfeO2ZLx/7pGU2tKIoJ3jdQWVxt0PkYWFAvTmnOV8znc5x4NndGNNWMs3f3mbx4A+tySsmwpqGtZhSlDiUIquDjoI3GW0G8jRJZyEXQNCPU7gY6s8xPVzSzGXkpQIttKmy9CvkHotu0qBCqhThaa3HGR7rlUZmJqzJHuRDOZerg6dw8PGT54IjNZy7DmaUQ8EYTOH6DMg1ajWhdjrNLlA99XiofihpFSaN2jhpQeyWXn86496RicaSpJit+8k+/TnPwAte/8nGm127A9RtI1VIval594wmv/c532DlckBlNeXkCDZwen6NPzlAjzZMTx3TquJTvYjZHXL68x9V6TnbvGLVT8uZxxStPbyGvFTDVlFd20E9tkimDe3JGMT7H15ZX73nOzzW/8jeeYeSnGO1ZlI7Jc2Pe/9qbnD6cc/VLtyj3xkxGI05nNfO3HnHnz96FHz5it/RkT5VkV69R7F5DTzdxozHkBrtcQaaocoPd2EBNHHq7YXRrk/nbh7jcYQCtJeSb0h7JBZ2DyjzKCF5niBlhdQ5ao4sSZUpUluFMHs8blApAz7earAXJc1w2phxt0HIc1ryYEIpKZPY6pW4N+15JTCOcaZwEMID31E2L0wUmH9G2DpvnTHeukEnB0cP7tPUJ471NRrt7mOkmyyePsKsjciV4V+JMRUtk5q7E2YzGNjjx2HnLQj2mHZfoUUGuTCD1kc6lJDW9JE5MAiaB+aXTvt/zQ3LUqeU7bUKgP0Hj1dNQSRFTnf9AVEFLzLbXMfTkxBfpW0fHYv+iebFzAIwdXJN9JIKKJH/F//rivdEqLT5oERBwOkZr+57++wg41EUaGgSwXq0Qf0/pi7v2wA80DDJ4RlcEqhvjcA5jgmafcs7EMcZnrzFkCZ4owWfCdpH2IdhOOiySgELvhplSrwfuqdK8E0ofJPNAcmSMXD9otEjDdYhS/39wIMR2SFApNQhBSUxufVH0mn4HtIOX4kOznsBsuwpLgxCSCCxETERHLWuq/W6S0+IdHml3KNZnYci5L9zTgQu/fs47QPfPGSalSEP7wPOHuDgw0B4w+OQBcqHLAy4+zIa4xtyHC/6iGSR1P62qIUK5eN71jJ/43fbgIJ1XyZwQEX+PSTNOljVnsydMeQqVb0NegRthck21mFNXc1ZVy3g0CUS3sVjlePD66zx74ypmkuP9Jl5rRBz17AyVZUiR0zYN3jqU5GhjgvNajN+WcgO9scFieUY1m5Mbg3U1q7MjxFmMdZgsrDdvLdoLvg2hm8oY2lhyFDzeWVTb4qsVdr7ELVv8UmHnDs6XrF57wN4rz1E3Fa0HUzuMN/g2R6sVrffoLFStcyZs40x8kNAEWgEtnlYbnvnUFcrzc2TpsKcjxizYyeDwX/6A/T++x86n97j82RuoQjjcP2L5xjnTDEBRZoLLYOEapltjyj2DPV5x6Zrwg2N4/IMjPvHp6zzza68wuamZfe0nmLzg3h/sc17N2PnkHm/deQwyo906ZJqNWDQNP/rTA37yk1NMo/jFm2PKZy6TXb5Jc/iYYrqJ27Rsiuf9332De996lzfGGVJk3FhYDs5qbi2XXPaeaVmSbV3FjC+jyJn/4AHjTz2N1xplz7FZi6bBHTTM6gVmy/P03/k4b/zTr5M1FlUoyDxkocKhjlEEKlchuanS6KxA8iIwfZMTIpkMyphg69ch5bRFwaiktRZpc/y4xI9H5FbIJ+OQp0AF+qNzhTcarUwgVT5oDdq6DdjA+FAIx0OZj2lURtMIogzl3haFKZgdnbH/2qtkU4XyNf6yYbx7jWx8i9VBhludh2Q8lOioFlYu+Mlk3gYfE7GhImYmlJNN8jwJULEQTZJA/YBuSKJWjpSkTXysj9PtU4dzgotF5STeFOz8SSrtGXKgvkkr0Nua/VDyTZJ04JprGoT4C50JIT0jCoSJrHn6c9YHp2UVy1PHrEQIIXeIqAGJTcimQz7BQXfNubvvSH/Pmt5cOrqXshmGP34AtHqAE6Iy0uTHdjpBdcBPBuS6l+382u/hHUocexB6vPTAKuVBkDVfsXSvdJEk1rsOCIR8Ci52IXxWg7lI3MhdeE8/6/jIYKAw0KqQ7S2MKbycdb4TF1J6QQKdYxrDCz2pUlPc8fSmhA669fZvAlHwnd0nXtu9owvS/xqTvIgahy9QBr8P/RLibz6Nxa9133+YDSa9cU9nFlAI3g3a8Bf7wsDulA41uGYADIZHqtB40c413ATph+Gz/fBD0gAk9Z4jVexSHpQPpYCxLTr5FjhHmWu8hvP5PFRpy3IwOaKFLFuyWC2xjWe6vYm0LbZpacWidc3i/IDj1+6w+9kXWKkCIw5TSIwemCOuQWc53ufUTRMdGB3KgnclaneEyAK3qsilpj2fU61OGWVCZnQ3JG8F2yqcdYhvCM48FsGhlA/pkk2JdmDakEO+1eByQZeKdmI5+sGbXP3KZzFP7eJOTzGuBedo8gyVFRilsUYwGejMYY3GaYtXBlSLWKESyG/v8cnP3ODJv/o2uYdsVXP5U2PMziZnD+dc/RsvsdoZ8dZrj/Hv3GfHW67uam5/3vF4MuL89TmyPMPcqzl5CDt7gn56glIjivsr3j73/MH397ly/5wX/uYtFqsW6pprOznLe0f86J37TJ/d5f3v3WNx2HCC5Ud/+gA3t3zxc9s8dbnFuymjF3dhp0QvJtTLU7wzZNe2mL7+mOZowaMnhrt4/k+XFIdVS+YVRalRT9+gvLyH3gkZ9fJPXmGVLZm/dZejN+5i53Psco4aOcpdy+TaBvnnX+byF5/l5JtvYk0IT0MLKgddCpJ7JPeYbIQyeVRjK0KKXR1owWD/qFg90DkJKaqzHGszys0N9HmDcXN8FraWMhmCQcQhStM6h1ehWqZog9EabzReR4nKGJrG41sHuUbKCW2u8Lln/uAhIguaynJ62DJVlix36O1rlFefYXVwH9OcIAjaJ2cxh7UKcQqjQma+JhfQBVoMuBj66GO8veqTznSC14DRJF2sleQ3EB3SAESRWMU6/Yh1CS6QSy9BSk6AoiMnSZqNzFMS3QovIO7T7oX0rbkYeTYkS0SAkfIJhO4QMpEKTvnID0LlxwBakknZdQVKxScvNYkSf+qgoqtiuJanJdpuo1DUqeATvY28xaexdfRVepQQc+l0898NTfgwYXItGsGDl15KF+Xib/2bDXhn4AcwaL6fQ4msNenfE0jwnXkjaRTwSRny0VQDHxkMZCpMRnw9HVJJ4RN9cyqiwa77dKPuGHFYdLFWU1J2DO4ZXjuIFMCTChis4YtOHTREf6n5weeLtqIPHLGNbhd8iG1+yHDTON3grXX/hkBp0FZ66+nURWDhfIjaGDLuD4tc+Fl9h26Dhlt6UNAP39PV5AaShStMb4oisPjumohcnSNTDm2ExXJONT9jXGxgW8hNRl09xK4qinyMZ4lvG9oKWttgRNC5cPTea4wubzG9eRm8otUFyozIsbSzOXa1RGcGI+CVwbmQmdDKiExbpDoPNuD5Gb6xjLMMpcC6FlEhf7tWghYf8mK1OoABTLBX+rA9G19jKx9yt3uFKEG0pcwVdktYncx479/9CTf/i99gtLmFm3nEL8itx2eCMzF8VoPOPJmCJheWuaAKgVZhbuzyxb//CVY//QsWJ0GrsnEZ1PWb+I0JNz63y5//5CG/9//9Id99cM4NL/yX4tjaNbhdxcZE4fKc0/dqlvcdVW3JntvhYx+/zqPvH1GI4Ve/dI0775+yeLTi9FsPOXUZbx83fPnLT+HHW5QbBzzzqZucfS/n3jfe5eQMnru8wVO3ctrVGQ/fqVCs8N/7MZe+WuDGgj2Yo05XQIPsQX5kmLaKW67B7owZtQ2zmeOVj02QLaHBkrkF87rl3jv32X/3MdWjE4oaJhlsXdNMx5BNNGbsceeO6a1nOHv1HriKXEd50LueEsQkP0hIb621BmXC3tAhFDDPdbDFSzQNGaEVRaMMqizxouF4H6dnSNRGdupfNOKETAUJPKXmUpkJz0ehtKL2gM4wWtNqg5tkZNubNA+OWN59F6MsosBKxeL0AK0aJllBtnmN3F+mOajBVigRvGsQBZnS4DVWNB4Twipdhq/PcHaBw3Qx40FaDKEMndp/SBLwcZ8mgaqnpAohFSMaErAgqMTwiIE2gbW709Oj3KGS/TsBgdSZRJcC7VWJi3m6bH2BrPXOkMoFPp2kWydJFPMdzQ0kVKHEdTyn05KQ0uwO/BI6HpO0JYm/pKH4RBo7TcAFqTG1+gFz9prg2UntcoEup1m8yFcuzBUgPj0vtkfUNiSn0Ni9BCYcSTs7zHlAl9Q3tCJrXfQ+pIQasrO/6vjoYCCpNpSixWNjB9OiDXOTVEqDCUmOgkOnNz/QAvjgaBHUOJpO/T1AmogevNhuF/Tn14xRni5jVAfJBi9w7eYLR3L1THakboMNNtkFrJEk6w4sDsBCShDR9+FnILThLof1vxfBDXRMPKFwP3zOh2kC/KA/foCQO6oRwgY14bzzIa98V4ozPlcIiNy7lkV9wOxsk7LM8M0CZSyL2YpCFzgLVetYLWp84zF5RpZlIA5p59z73rd4afzLqN0pNQuMUmgZoceaen5Gu1ySa8GrUN7WiUeNS0zZsDg6pa4qiszjtSKXnKwIEp8y4BuPqxy+sSEsrA3e3NJ6pMmgCdkLFZZWa8hKtPPAEs8SfKicmG94zu++xfv/quCFf/CrsHGZ1h5jljVCTpuPKNQEbSyiF1AqzNKxiXDYgr61zc///c8wfXKf4zsn1PMQomiu7aKeuoHZyPiDr73B175xh7tV0J69iXBPhCubsPOla7z3+4c8tjXvLDVZG8LW5NU5Z0/eYXfsuPTiJs5pXvzkC9x1Dxlvan7uC8/y5Hf+gvLGBuNrwuqe4/DtEw7eOGHns0/xwu4WHHlmjx5x9EYN04zd7Zp7f/QW6som27tbFPkMe7Ki0DN2n1I8Og3FoV4iAy988hb86Y8bPpuP2LgUsu1988cPuHPvEH+yJPOeTTQGD8rRZI5m05NfGyFbW0g+oi1ydDHCnTRQupB4xnnEC0plGFOiiwm6KEJooYAyGpVlqDzHKgWiw/rwIcRZlMG7nCyfsDxxlE1NuTjF6wJjwrUoFSQ0wDYelXlEG5QIrUg8L2iVB4nUebxoFs6TTUaozQnufMnBH3+T2s6QkaFpHEWuEWtZVUv8wT3GOEYbl/H2Ku3pPTRgfB76KUFNUdtx0CJlCmNGTIspRpnIWwZ0Toabeci6E2OTwXWJ8Q7IbKIVa/5W/WPXXK26ZhPqWOf3XZuA8r3WoivU04G5LtZsTaMAvXDtVUiMFATISJK8w3uFVxbnU3ob6c7jfahK2lXAJSEUOjOsj5qPtXmMYxkKZr6P/++I95DUdvcOvQASbZYLBP8i6OrBRfo44PdhztZwVwRGSWDpsUPsXtIg++jfMQjpHFyT2HHnhPjXQAMfGQwYFdwmtAKdMJmTzpnB4fFqOIo0cTo0kwJ1veq/e90DAAy4BAr6LEqdE0cHYSOzH76M7n24vv20ArvjAlMd9jFJ9mu/p74OrvG2R37d4ovBIUM+nNr3DBYSH2Tqw2PI+IeL+APHYOemxQzr4SMd4/f97z5e082L7/vmgo+Ajs+0zoWwQh+Zv42+Ay5lbVNY5Thb7lMeZmyWmtadoH1J1cyoFpb5WUVuDKNJ8Alx7TKoZjGUbsF73/8ONz/7ObLtEWq0QdWehciFYgPFkno1D/2rHeIzVL5iVc+pTxdsTAtAUE2NaaCphMznOGtpG4uWDJ/VWG3xWYb2hiaCALEaVwuZzhBTYqcr2sWc9iwUW3ELx2LhQGmmVwrM4Xs8/J1vcPM3/wb65i1kMqUuDmi9Rs4srVvQLg3uWJCZwRWKq7/5Are+9CLu3dc5/9E7LN51+CbjXHtGV7dxT2/x+rff4dt/+j5VI4y8R1xGK4onHlrlKa9tUty2vPadfRonTKXmqgi5sjy1mzN+4RrF9i1kOcNX8MxvfZzf+3//GT//sW1ufHbKqz+5zzs/qNi7fZm3757x6N4xv/JzUzae2WV28gjeP0bVnuqoRd+ecPN2Qf3dN/nW6+fcqzLq8ZS8WtCsWpbLgqfVkqdHGrdc8cJn93j94RPuryounwj/7s9eYzVfsa2E0udkNOBbLGC8hCyL45xyY4rSG+hyi9oB2uCUw+LINEgOqgBVZOhiiugMZTJ0ZiIIKCELvgIq+RohIIbWZzg1wk8m6NpiRjnF+UNEr8iyaVhbzoUwPxHIM5TJaQ0YnaGNCSl8jQk+Ud6ENNeiafDke1tkmxNcZXnwh99AWKFyHbJiKk27rCmnI7Q2NNWc5ugR2ivMxiW830PqOcYb2sbgfNBWqbxCjEN0RqkUG6MCrRW9nuIDOz7+TQwjCEpdvjsfbM4SiwH5DgAkKVYGZC4ZGAZkQ0BwUU1Ofy8hU2C6epg3XzxBs3FBovYCOt7rh0gAIGYuTDROItIQFdLzelJa5PC7Igomqs+/4DpZ2NNlaUosZ+iAvo5wBjOaaCADWs2HHImZJwLvB7b/v+ToQNfgPfaR+N1FLj4pSP3puX2yOBd5QadVif1VQgiFHSCC4LiZmvYQ94iXv7Sn3fGRwYAeXBxMfAFFi0Ab7Vsdwx5GBqAC0w93xcVrwnfReDSgEYl1nNbiQ1OQRprFFLMXJnLNViDD73HhD1f7WlbC9FuPZtelcd+//OSxuqaijy/P+eR50y0ocfT5/7t2hv37kKNzDBoAqaRV6N7uhePiTu7g52Dhp/a6yonQGQChBwfeBtt8nD8X2w9+AoN7AZNrsrzEmAkNM1aLc3bLS9T1Ict5y/xsTrPwGMkxxtO0NUo8pRacNmSS453CL095/NOfcuWTn0JKQzYuseLgXHCuBpPjXROKEOFAr1jszyhlE1qPWB/Uba0jM4LWBi+GLAfxitYZFC2qbbFtExy4rIO2pm0r2tbhvMU5aP0IGY/ICoXZNUy1w5gCN69oD45wP32L+4dnTL/0KTafuUF+/WmyZ27jX3nC9HjBal5h2xWZUSEb4sEZZ9/5Fou7j1m9ZajPFae+YVUanHdUB49483t3ydsW5S1KPEscx1jGIpSloiinZJdartt77AGMS2YKaBuqA0HpY9RYUYsn311yLuf4TPHtb9zhqPTk04Lt53Y4+cE+G3nBp75yjYPv7rN8fcbepYIWxejSGL9qePLenOnNnLMnLXffq3hiW479KfeV4ido/j4tX3LC5NYO+USzqHf5wsstSyPc+f4dZLZiy3u0d3hZdiSwwJCLxkqNcR5WI7LdXdpig/zkHNU0ZCMwpaBLQZcOlUso82sEU2SxSJWO8f7S5WtXWgenQclwkuFNAeUIXWomkxL76ju45T6mFOrVglSgR3KFMyHpkDI5RZ6HTJJRC5XolHWOBlCZZrK1gd+e4FaWw++/gZrNqTOHdgrtgz9ApTVVVYckTNsT2qZmcX5CmY3JRpeoWw+2xemg5TB5cF60SiN6hPZT6qahtcHmnSS7JBF2nxnQ2cRMo0+FS8JpZB490+5V74nODIIPw9/IvDyxTFLHLyMYWKPribb0TFiQroRMignoTfZhDIG6JH/5FM4Y6GKXY0JcbC9m0hPQDlQyLcd7bKduGND91FfoTAq9cLdOJj9AIz+MNiewuXZvN8nrAlxHewf8Z9h0NKF4QrZN74OzX5e5fyDBCzqugeH9QwNEl22iy+6zxur8+hDdcAx/yfHXKFSkyERow7jo8WtqNoQA+lgBrJP8yRDRcdEFxo838bshFXPEpwWaFin95HaSLD3K7ZAX66hOLvxdg4zpiEiUxOQT0xzeEdCl2C5NR78OYqIkFRdLN9kDdXr3WD9os1tMg9/W4HQKE+m9KNa0UGtgZzC2QXbDHgtIP2fDsad1i4p2pXBOuRD361yqUS5RbRscIWMwDaPMMxnlTEeGUhtc01JXM2bzBc3SkpsRo+0AIrx1GMnCohUfbPsBsAZ77PkBBz/6C/ZeeA4zNWgxtFmLaSQQeA9iHe002FqnagsRcK4mE49RE9SGRWeuq8HuYqSE2FhkxUQJp1WxOhp04atqjExL1DQj3x4h5YhmsUA1NU0FRduQXd7AXTuhPjjm7Hf/iFlekm9vkN18BnVthFOWahXKHrv7R7iDA1b7JzRnNU9OHWcryzJ3HAK+bfjCo0N0tcHV21u889YJE685in3fEcNT04by1g7FzjZnzRMyKdjyLRu+IX9+l4PGcfDGCYuzFrErxqplfHXKxvs7/MKXXoGP5fzT3/4+V2hpH+3z1PUR54/PeOVXn2e/aTh+54T3Dz3TbISZLZiK42y/4Z0/OGFRe4zzTLA0otlEcQPLl5VhazyFouTax66zeDDj8nPXWNy9x9FyyW0UjThaFI0XFJZCCUYsbHim1ydMb2wy3r2GG++gy5z5w33a1Tl6w6C1BW1RRqFMER35CAzcEXIbYHCuRXyBwmCd4I3GmhFObSHFGD0eoUaas298j9Vf/AWym5M/dQUzneCVDdkhJyMykxEilUJCK68NYrJYNdPibNByTja28aNJKKg0qzl99Q6cnZDvTJAmp20qPI5WwGiF5CVN5WhXNeOtKd4L1ewQtXEZZUqcn6O0hPwFYhHKULhIZzSV4ujslHldE4SjyDr7uLFuC3da08G/xKcTiYQQB+C6SHTpt78PXvhRVxuYk5JOkZqeraWnNz4ChfBoGTCcQNeUj9oDH/uXwgpdSPFscV27yWku8c/kY5boceIvAM47nGg0EsC8kugj5nrTRSd4BulYcNFFbSAcpRaSsyGDwRIf6X1Pni+YE8Lt0V1PBgCmn9rIliKtjPwsNaWUIK0P+RMgRhL4PuguOih6t/62JJ4akO5okhHMkG156d5UWhVdtN1fppEeHB89z4AILoY9ZN4h4nESiLBDumxvwW0lSfwRaQ99BLq/hpCNMKHW9OISo/f0YnecnouqnwuMtJuxbgIGSDaFbAzs4D4xyrW5iqt1yHg9rKv7pWtSkooK4mYYOg5eYMIfOAZgJi4kH8NwEOkX2hqoGCLOfiz9NCRgEMME0/j84G+6J4oSwflWAiNNZT+dQyf1k4tVx/BsmJYts8FGcYlxvo/1K06XFnHCaFyirMVbj/IGfEbQOgheQrGXIOEolCiElvr0EY++e8SNT3wSNqaQj2kyh5qvQoxs3ZDlgaTVq5pRMUKLxujg/Nc6wdkANER0QBuuRbs6eBw7Ry6atnG4dklVWWy9AcUUvz2hKRTudMHq6Iz27AEcP4TlOc1Zy3zWYI1CCs32zpTy6gT76Ij28SGLV99hdaKoFo5Vqzmua2qnqaVm4XxI3Xu14BOfuMr0Rk4zW3Jwd8ZitaLcr/jUl26zWmj+6M/vsNNaatE8k1te/ljJ5OO38TsbvPpwhnENS69QS8/o0PHpX3yGk6e22X90yP19xSRbsnv7EtPxHkVW8up/eI2nFnCjrVg+aHn6H7/I2/N7vPM/vMPOtEGPYFbD/sGcUWPZEMPUaZbnLd5qKvG04lh4zT2xvCyal6/uspyUKJWhRzvkezVy3rD1sWd4On/M+NUzFvsLGi+0RrM1VpTbLZMriq29CaOdPdx4G3XjGmZzDw7P2X/7PrZ1FHi8ceQ6Q2JxLOMDIBAT7PdOhZhzhca3nko58nJMqycovYXenNBuaHJR1G88xD44wuQ5y/tHtAczeOlpNp99BjMxiG1p6gYnlkwXYc80gUaJVljtITcU4yl2Y4rNCuRgyZPvfw/aOSoLyYNMpjDZiLax1LbFu7D2ilKwtaZaLBjvjHHe0NYNZZ7ReI/3LRJGDVmGSI4SYXWueevgAcsmCUZDPibdny5uXPprgmU0EBEVaWwnR/o+ic0HyU967joN7NlJZCqR1qwJvj3BST7dwREwaUgHZDukOpG+ucSyBs/y8YekxUj29eQVlYCI9w7XMczYb71uG+9I4wcYYS9d90BmnaP3LCYkcVrjOYO8OJ2w9SGHDK4K0xw0N15sz8xTmzEfgkeF/BFDCXAgz7m1FgP9dtG/rg+PjAaHdF/UjvxMi/OF46NrBgCrQoeUD3aqFo8VFRx4/MD276MGAI0kU0H6J9FL2KfrBsz/w9QsQBcq0sVlpPO2f5FrzNHSF0ryEf5GcDGc7DSxfUOAH0j1/oOXJtOAInqix3453yf46d7ah6HLD5ncwcZJWodYDowO9HQwOHbkAtpL9rOg1k9OkHHIaWF0zpF9aA3JFOCjatJ7tPOdJqA/F9KyajVCSYHOJph8C3JFngvl0iKrhGo0zqm4QuK9KSV1nNfWtXhXoW1D3ax4+1vf5vLzL7B54ya+KPGFp60rvMpwc0+R12SbOZ4G5y2tFYwSdKxoiAXra1zb4Js6hlaFeVpVDW4xo11pGrWLXN7AFwX12Tn2wRn5YomvjlH1MVl9Tj07x55YzEpYnUO70JyenVI+VbL38iXM0znjnTnjx0sWh57JiSeftVTKc5YrNp7e4ZW/+SI3v3iNpqo5+uk95PiIl2876uOW2b37LB8f8Gu/+WVufekpvvtHb7N3UvG3P7nDrc9dRz31DH/0hz/mW3/xkC+KMBNLphSLoxOO3h4z/nuf4uajHXbeeJ/332hwx3MW1ZJX3/op06eu8J/9k8/y099/l5Nv3EG/fsrT1vDeEp6c11x6ZZvNkWJ6uODB/RWPHsMKH8PKLK2H+xruieG51vNb0xJuTtiaXGLpWmRjA6Gmfu8xh9865OrffIXrP1+zuHPI8tExrm1QGWTbY8x0AzPZgs1NJlevYje2aZbnHH//p+xtlmxtP83hyRmmPQfd4kuNMxLS+cYwLKV1MBU58FJAMUXnU+psC0YT8o0x9VhQx6ecf/dV5GTJdGMTnrpB6yrqew+of3yfg7fuoW/fYOPZ25Q7l0LUrAevLFpMIKx5hpnm+FKTZWOq84rF+/eYv/cGYhqcCnkCAoMUlGiyXOOtomlbfNPQOoMZV7QrWM3mjHc38AIN0MoEaSu0tmhlaFAor9A65+jRKT998phlE2zinQRKYibSySmiBh733f+RdUsCAhLNuL29OYWtJbWy8z6EYkagnzSuSWa4IFNHlXagc507t4/SsI/1apKGQRI8GdJsOkGnr7fAwIK7ThxlSOMG+VeS9KsirfbWdXPS0/wOcgwsG3GWhsJT17VE9AcfO1NtL6T64f2xXkLHsWMbnVJhMJ/W+1jaZ/g+whhDnqCQWyJoNdLDpJvH9LXjEUQ/vXgyVNUcCMExKWAABx8SFfchx19DM0DnuOG8UIui8XFCkj8A0R9AVEBRXuG96hdE8h9IzoRpRtLLE7mAuMLgkp2oc9noZiWqonwHI+mYfwIFnSZiEAIpg9j+pF73Ppg4esUNPdNV3eKQBBJsz8H7tNcDKDccR1do6UOQwIDHd0u0M4Os/RjnQAihRAPEGv8lm2IPivw64++G5Qd9DpvrwjbsPjmB5M2Ld9StxUuNaI3KNzA0KF2Ba/FVMCmI8p27g3cepUPPre0rXDpvY9CHIc9ADBzcfYfV8TnTa1cotnIcGTJyqConzxyiKmhrlI3pTbTHi8WIxrUNQo3WDnIBb/DW46slvq1xtaPK9jC7N/GcMjs4RtcLCrXEjBcgFlVDa5eoBqRSWOdxuUNaoSw1i7tzmgctN39xh/zSNaqzBzg/x+QqqFqnmlu/8gq3f+M5XOV49/fe5r3XHnP28ISVtSil2N2x3N5oUU8q3vp//THP/K+/wCf/D1/GFiWbhebBkzn/5t/+hK/92x/zjMDYecYCG7lQXjKs5ufIT96kuDlmdbBkmnkmo4z8E9uMvz7j+c2Mx3/yDlsTYTYZU717xvzeCZcuaR7uax68PmPv+Sm6sTy1B8/uFjTLisVDz8FKqCYFz2yX7EnF2aOGZ17cwlzdIJ8W+EVNhUXtbmPfe8Di/ow/+hc/5pVP32Tn6mW2fuEFshHgNGIKtHc47fBlSesUzZN92ixj9JVPwGpJ6YTbdcP8YB97/gTXnCKVw5LhVIbJRjhv0HoTU0yx4zFutAHFBmyU5JMx9fmK+Zv3qX/4Jnldoa9NObUL8qXBb29RfPozZJ/5OZr9hywfPeLRD36C3pqwcekqk8tXMdvbZEUBJqeJpq3VwTmnB3dZProPboUuNVme4SVoqpPm0NkGXKh+KErhrMZaoV1W5CrHNcJyfkqWjUAyVC7YZoV2BqcDkTZKodsRbzz4Pu8fr4KEqECGNCDRhLSFfQwGFtZoSndPx1iDWJgYXh+WFvS2iaGnUsjIIAdgZDqJmV6kEcpH34AB/e4EMgElKpSqJkCVlGBnTcZTMnRH6vsBHX1O96uo2R1kW+hlQhfCnvt8NQwQRqKTYdxrIGTAAzpny3iym7OOn8j6iURQ07l0upuP9OgePPjh+JUMuuWiU+IQE0nqWrzXx/N9P9PsrmWFHgCNBMX+oycdapVgPVhCYdHaS4jj9XHAJM1A9BdwCVGlI4GCgQpLYDiSzumjuy3NVoxCHeYqGAIG+ont3n03i+theGEjJebo+mev/dcz0uDjkKIUItqNtnbvkjvPYCP0qyD8Xau4OOj3EPSmjT08hmaPuKlxHs+FF9uhSumZfIJUHdAZLPwPjC+aAOJp6/vvPt0nQVXonGO+Epb1iraxeBmR4SllhSMk3FFi8FRAi3hDKpf6gc2jFaFaYYvVQdIqlFCdPmZxckixuc321auYiUZUTl3ljDcEXzdIA1prlA4mCCsgmUZ5g6tr7KoOAKBy2Nk57sxSuS3Gt/eY1TPaJwsKaooSsBmuLmjcEuuWNHWDXVnaWoWkRE7ReEtdePKlZ3Xa8tOvHfLF39pCdncobIs/cqiZ5vZvfZzdv/UpnvzoLd74Vz9l8f6CeeNYqJZVC5UId59oXi1aPn09Y7Jo+d3/+9cZ3dxj5+oGT6znG/eP+eMH59yoanYFRigmAK3GHze0hzXtnuWdnyoWr56zN4Zsd0GuJ0xqy5Nvvk921WAXwks/v0n57B7T40ss3j0nyxY8OD7irR8c8contrm6PaLRLcZfZb9d8vKtXUY7Lfmp4vG7+9QfL9E34eDJgsWsZqNUXM1mmOeewR/s4e8eMnvjgFcfnHLpqQnPf/k55iMHkw3MaEx9smB1cMrp+49xixa9Ydm6tcn4mev4KzeZZznae4qr1ygzB6slube0qxWuaYJKvtxETTZxZYGUU/LphHwUaNHJT+5w9t3v0Zw+xuuGZZHjznNykzOrCqTaIrt0CRmVqKcukT2zh8xn+Nmcs9MTjh8eoMqccpSDDj401A1+scCpBZkpaEqFCzmT0XnRmZ6Ss5y3nqZtY2EjRSaa1cpTzVYUekWWG9xyhjYjTJaHBEfe4MUiKJTKmR9X/OD+qzw59/g1hn7h6HkO0KeoRSSG6CWVJZEUJ4YyoBrSAwQvAeybSL86ijoAB/3vffjgRWbVfkhqo1R5sKc6ieqnhEiDNG8yaPNDjkB7hqWVEy2J2kbp6V4oT/5BSXgoNHdCUHfPkP7HNsWzFk12oY/r1yeAkcCCop+NRMfjO/JJ3HWRsfeCXJChYrREZzKXwfWJdgdeOEgtEX/2ITR2LXfNED795cdHBgNLHzaLR2jwITQo2lMEE16AHzD6yPjT37BoI0CIUrykBELDOf0wprgGDoar9uK1rr8HwmbooOcgpnTIkON38aqPTe2Y5VD1FA8XwzcuMvchok4OfUgHdrqMXQO88CG4YHDtUNIfzml3UT/WON5k8x+sjgtTlEBQH3qY/DFDCc2ANa33ZNCZEBLKBOFkteBoMWe+gqlMMN5SmDGybZjXp1RLT5EXeFfhfUPMhhpzZveESEU7Wls48ArdalppkawFlWOX59x/+5xiPGZna4dMLuN3puAttqlD7YIm+AMkIuidRtmcTDIat2K5XOHPG7wtMdevsagt9uScLGsYxXS/tIrqbIZZVqgGPCMYz8m1QlXAytPY4EzpS4NbWfS55vWv3+Xnfnmbcy3UjbD1659m9zc/zdH3fsh7/+IHmDPIGktmwVhFEd+PU558KXz3XcdXrimmKuP09X1O39yndZrNzPPpac4zz13l9gZwMOf8cMXovGVWA1NYvLHk6AlseIVbKNyh4/T33+fkoGIyKcnvWfZPFly7PeKZ64rlwYJCKcabntH2Ds++YLjy8iaPfvw+yzfOMHsFn/qvv0z9Rkv9o3tU1QGZ1JRFzsGfPuDsRPOn3nNXHF/+6SP+xj9quPLiLts/aSnFcPmlgs1PX+K3f/8ezdETcqdQygegaD0byjPeyMgLR/XklPHjfbZvPyS/egO3scdqQ+GrDH8Op2crJltTtp7aQqY5tiywPketLGq+orrzkIN3n3D8+utQP8bLHClsIJ5aQeZZZh6tBHuQ09wdwcY2anwVtb1FvruL3rvMxq0bWLvC7p/hqhXKNWgBV7aoUiP1FPGeUgXtZpA+LZgMZUJUiODRmUb7nKppsdbiG8UoL1nVNfOjQ4q8ADPH5QVOb4S1qkCbKZicttW8eectfvjOO8xXIFkBrulAuBLpQuoS45eUiyVpOIXgHBn0zoFqDJhxF4VNn4wMpaNk36dlSXJHohVJ25lkMDewdXfmCRWdxp0NTnJe8Cntd2JkLtLeyJEtvWA2pG2JLQ9k5DWW0DHxKDg666LGIZKWjrUHc3AK+R66DvikrRgS3aHwSArXG8Ku1L5n6HsW2ov+C8kkkMbU8Uc6ADYMh5f4XsGH5EwM+9/3yycmP2SSaSwdy0lgJP6exjPUNnyE4yODAYvQ+pBsyKUUwilvvw9Og73U3/sISJdXwAQnmy6vQJRyJaLZ4Wi6cUs45SLB7+wzvreNDSTc/rx016XpSouiQ4Y+SPg+VrgLL2sQk59guAvM6gMAZAAk1tT2g1UWLlVxU8bnXUAAndrKxQWSQAMK73uJv9uxEMecfAJcf88Al6TkGj7dmyyF6XvyK4jpKn2cIw14HzazKMUQ4yLCrHY8OTvl+PwJe9UUU5Y4JeTjbaatpTlZYCuPsxloh5Y2giwBacJasilOFhQZ3lqcaRGr0OUe43GBVo5yVXF8+Jj333mTa899kuzSC5higskUzs/xVqhXFi2CFsFbwbUO2zRU8xVu3qCajOXOFYqyoDk9YiQh1NE24LUl9zXGeprMgC4w2QJlMkzhaM5DuGXuQRtF1ThMESKhjx9XPH7jhLJssDev8dRvvczJj37E8Xd+yHSn5TzXaAvZ3DNCYcUiAiMPNwqFU8KrBw2fKAybIoxEGF9S5J+5wfYvP0NhG6onh4idcvpwzlu//4TpzNPUwp26pXCCyzSTDTi3Nbc+s0Hx3B66hPf++IDGw/EPF5THP8HuW+oZeOtxJuPZ/+wVqvMZnM2wyxxHxWrZMHt4SLO/z+LtGSczS7Zsuf6lAvNTzyfvNpwC33twRPMvf8Tf/0evoHY2mU7njHYM//p/uY8/bNlSIWHVWDwGyD2I9vjM4UYWM/LIqmL2/nsUqxOml2+h7DXqyQ7+ygZeC/d/90e89fYhG9sF7GaIcejVirZ28P45R4cVm5/Mka0WX3sa5ymyDJ8FfwWTCTbz5EUqbjPDrRbwpKBd7OK2rlFNNlDTMaNrV8ArmrMZbn6OZoVHyEoVIg18vy8VKpjHTEbbtjhn47YP6bCNUlgVEjRlaGjh9PE+E7bJsxxXGlCaVoXQW9cKzdmKu/t3WViFMtH/X+loe/agJJrqFIM4tEBHozmu26ESQy/TVx9SLSuVhK8wDud8yLiZwAI903WxaqHqpNbex0Cp4CuGD8Aoecu79JyLXDYijPBs6YSQxGaTnX+dlaUjmgQi4ElhhmHIvSQeoohUFC6Cf5L1EtzRvA3vLjl1qwRC1nPY4IOD6hrlFoixgHSVCb3q+k5n0qYXrgZCWKDVETgIAaS5PoItmV2V9zEqz4U+WwHRIYLLR8fJiGYkakI617k4Syn3gDfhbXknBJ4bDPe4i3P74cdHBwM+SIy2f5XdXx8npGfEqatxQnxQA/fFgwZQtLP10z+3Y2q9JNtdE6Vu3103AANr/1I3+g3UL9ao9o8LPjm5feiRGG8MmhUXmGUIAbngtNI1Ers15M6DPymDVH95+pwuSOCj91VYnw9YR64RCHV5A9K1CVGmm5JZJACrgJ3CPcoPHCAHR1q8QkgKsqwU7x0vefTkMU+d3ma8EQoAFeoqsq3R7ONOK7IKfE1I4qIVKhOsc8GO6D3We0Q82mu81lhvGG9vk5cZJqtYzlqW8zmcnuCXM05ff5Mbn3kWW4xQ2iGlxa0aVCn4ZUvjPc7amH3Q4Z1BcqGpBTMZ4esFJYIuRxjV4n0TzD26RU/HyMrT5hluqVFOIWZFYxxKGUQsTlm0ynBZS7NUGDyP7lXcfDHn5q9+ier4jNm7bzIqLQuVkTuL3fZYFO3cMvIh3Gohnvc8fP65Ee6dc7RUbG0ppqOS8Zdu0n7paV772h1Gf7FPaWZsf26bPFNsPjviwY/nHNQ5VjWMCNqbau5wGzm8eI2rLzyPffWE09dmHC9qjg9nbJ9aam0ot4XFiaJ2LSjNeHub0/E25ZUzdj63yflPH7J87X2W906ZncLceZpjx9M3rnF10jB//JBfnMNfiGBqT3XnAc3Ris0d4d5BhT2yFLiomHL9MpSYmCxKKk0rGO3QGlw15/zxHcpFjVypMCuL393m5j/4Ckd/8Becfu2HmAOFjGt0Fhhf4xVmQ2E2PecLG/xNnMK1DajkqKVBBa2PykwM6wtMpW3OyZY5RoG3LeeznGxnyujaLrLcpDk6RrsZRiucC/UtlAQHOeVV8E3OhazIcTistThrUS7Yv5XxaO1C+t5VxqpuOTt9gik1443NUC9DBGc1jVMcnx2DU1za3mT8cMaiTa6AAQB0eebTNk4kIcbjB6bNEA90Uo9EYWp4PjE6F6XnZKcPQq9EBhyu6+SfSMtC80EjYgcQQgiAKWCO/r5Etn3XTqAByT/so5bWTYBgGH3QJ+HpaX6SyoNmpJ8zT4wMcGHcocZFDzQ+2GCStkN0lx/MW8e3hK7cc+J/Q23xukbXd3wraXcSYFgzpHQ89AI/AIZRdxInNYC3yEvVoK3Ydtfz/9hJh6wl2rOiRsANGFaXXSIx/+RQqPCYwfe4Kod8LCGp+Khhv4eFijqm3J20/bM6RjcADYPHr6uB0nN6tVSvWo9j6NZX/OBUb+Dq8h4N7FL+Z/xNR4fkLnbuwndPDwT8YOen8XdzJWmHfkgfImP3NmoFEvPvgUCKGk7j93HzJNTcHS55osbYfS+IaN5/suTdw0NuPX7Mxs41qrGitB6dX8NNGlR7TKiotqTxgrdgjEJrE5IFxffhnYCsaNyIzctPUYwLcAvOjw85f/cAThbQ1uSbJe3BEY+/8xOu/a2v4q1FtQqfnSNYZCS4VYUWhXiDdR7JMxwWtWWQ6QI7zxmPc8SvsMaR1xo3q3FLi69b5g8OMb5BZyNoW0QsjW5wOob7aAUqlNWVwuCXNcuFkH3sRezljPkPXyPPoBlB7hxmwyOZwmchm6EshcpaCi08ai2v1jW/8PEtFvfPufSpXVanGv3yM/yz//FV/uy1R/w9B6+MFKNSoy/dwL73Kj/UgvgFU5WxebNgYztj/mBOMXccfmOfaVNizjWXP36NRuCb7xyzZTUbjeLsvGXpM+aV5c//p3f4/Jd2kOmU5p0lT37wiNHeiua1BfbUc+Y8B0bzZ5Xj8Q9X/Ff/+NOojW2m37jD9dWUa1+9zuoHDzn50Qn25RxpcoRTlFbkLZjoJ6KJdSIyjxhBdFhxtgaxCiU5ZTkG3SCzI7x4nLacjsZs/MbHkarh5Pd+RFZ6xBi0bpEdkFpReUtmPM4LOuavz7SAjppHbxAyvDOgNTLO0TqEPFtvaVdnjDPAeqrHnrZxbFzZJhtdozo8gHqJVgqTarI0bRfW571HG40yGrESY+oU1gmtbRHVorXHaI8yirr2nB8doC9tgx1hVEnGguOzc5r2mJ3RJle2NtkaVSzPVxCdCBHpHNJ8zMoq8bfIS+gK2whrPgI954x0MsrTgZ/F6xJTTpRIJNYNkOgwnnwBVB/bH5mREhUk8AgEEuuke34fFdGBi6RlCWcHFFGtnWPYx4FwFci9D5oNCaGgeBc1Auk+v07HZEj6g0akf27KaRDn1PchfGs0Nc1Pelikz056M+vQJy1R1a7TPgGB5AApHZhKTTg8uhtjOELJHQGf0vH3HVKDOQnsIIKymBdnWDXxo4Kujx5NQKg0FQaX/qmoQtGR4SfTwSDVsGgEjR84VXST5gcMOaKdztFE+pe5BjEHR4oz9f0P4Y+nF5wvMM307H6Ruf6aIUBJ1yf05okOfBHwdDkQhA6IdM/48MkfIr2AStNKleFF9IBkYCYY9E2UX3cmHHg4E4FAjzBjpAQOnA0gqtOOhAXmfahN4AaToJWKQGIAPLxHScNi5Xlz/4irO69xefcK+oZCLU/ZyCfo8go4R+OOUG5KJg5sQ9vWIA4hw+gMJRawrOwldq5fY7Q5xddnnB/ss7h3gqod7cQwWo2Y7x/gpeW9P/1Tpi99gskLO7TW0/oxBoPzZ0iW0awqlIApMppVE0vcgjMFptS0bYsxObltcW2FW9WAwmQZ440NtBJaW1PNT8CGKnaStSgTNoqbtCCCyWqcMWxd32HzuY/TLucUY0tmN6mLllW2os48uRIcJd60uLOW7BgWTcZIPO++0/BLf2eHqcrxe0+x+bziO995lff+4h7aC7+N5m+1wsZPZjz/D7eor1zm9+1DXgH+8y/s8tX/9Dbnj49oTjLu/ukZs288pnptn+mz19m4uc2zX7nGUla8Os/49PUpk/E5p+8v2Lp6ievPbWAXNY+fOE5VyaW3Txm3M9rlipX3tKL4C6c59A13v3PMvyzv8Bt/9wU+8bnbNE3Ou998l7d+ckxbwe5Zzq0bJW8wJ3OWgjBXWjxGeUypMCPIckeee3Th0Tk0OJRvqL3FZCoAKRaU9QjVQqMmbPzm5/BHR8zvHGDzlnwqqMbjVzWtNWRGQWFi2B+QRZKTGTAjRJd4nSMmB8kATVaWOFFYJdT1irxUGO2ZP3EczudsXd3EbF+G82NctcBgEXGYIgel0EahtAYd0nLHfLw4Z1GuIVMGrSY4LRjtMVmLqSdUvuL48SHj8U10VnJyZjl6+GO8aEb5Hpt6i43JCfuzOvoISM9glOqYeCK9kYh0/3z0wenU1zEtcW+bli5rryBdbTfxvc0dImmLKnkHMYdAb4pMJAElOBse0pk3u/Lo4YEqZPKJJNz3NL2jeX1I5IAwRirXcQZ6F7jONbrvcCLBvmesQfDRKOl5SaBtFyLFkq9RetBAEu1JtVrvX2JFg7YDZkrPI5pgBoAj9Tj5UAzwTXrakN0FGh6qavpYATcAjpD+Lc1H4DxdUDl4MLGirY3ztjbvf8XxkcGAiwl1JDL7pCb3XRRBSjAUUHkPBkJhkN6BLoKAyODXeHwEsV14Rnpx3vUOGtH+LcFYNZzB/nIGoXMXeS0uNkQXztEhtP4/0lv3yaY+BAv92+1AAh92vm80/h1uiOQlevFi+g0UO5C2TTe6D2hM1qMHOhvWQBsiMY9AKCvquibFu+BU5HueLyqEBYX9EVVsBIWZwuBaxYPDljcfHnNjch8zuoaMK3JTUG5M8cUlik1NbR/hFhblNcbkeN+CNyjRtLbGesV47xLZxib4GbOjR8wO51ilMFmBna+Yz2dkRUHrPeWm4d5/+F94bvK/YnQ9x9djWv8E/AjXViAeayt83WJEhbKutUNOR6gNIZOY5tYEFbA4ha0qqFryyzt4EYp6iReHXazwSkAWiHO4FchI47wnNwbfenZeuI7ZG+Fmx/h8hBtlZALOLxF9jOQWayrc3LOyIRtiYRuUaJbe8pO3K37p2TELFfwl3vrGQ7a84VzgLsK/Qrh66rk9mXK/vMSIfX7RCF/4/DV++m/fpn7niNu/NOXyZ2/y5oN7jI9b6rND6gfHlNc2ufnUDp/4+Zcx+pzHP3yLaTFiemnJeVWDyXju81fIfuMTfPe//SGTu4e02rDSLSvnaXB8RUEhnod/8ha//+Y+O5cNs7nj4b0z2gaccUxaj93bwY/PGJ17NBajPeVIUWaebAKqdJgcxAiqMOiJhBTE+QitcrQLxYkyn+N9jSozmtpSTQyjX/sC9b/9Y9zxHKctOteYTaFuW1yuUKaBTJGVKtjHTQhJ9FLgTYZToZaBaIWoEm8LMCFHhVJgbYNkmiK36Mpyfv+YbNcxnUyDU16zRNGidCC8ogMdcjZoIyRmWxXphaBQVtnjM48SjXEZWWaot6b4FlzrOH70KuezfXS5jdUbZKJRmUdpQ9vtdukZOnQaAYgq6uQDEP8lAJESz3S57r2Ea+N5RaoUSx/yh++kfDsAHJ2DXqKTalALUHqiN1RcKiUfcBpcl5MSKpGeDiV/h3TJhaPHEbE96dXq6bl+UGVWpU51DtwqakuTGcRHGSuE9fkU6hf7lnrShXB2zniRXid+FOlrGkP/bfihH2rHIpLGZzh50j87ZYjtzMgdj5O1dTB8Jp5o0oogJGo7PlAZ92ccH10zkFJNdgxHEXZISCQUNALEv6r/66WLOe/f8oBRKdUNdF2ITxOUwgr7YSf0tGbfHiArP1ggiemGkzHhTmSGPiU96oBE7OSarSY8qPfsX+/eBz53PwyXdP/M3iHGdWr3cHqQEKkDORfQTFebIfXZB7AQkeMQNw8CdwYgLG4eH7QFyvX/OptWv7M6gOHwsY64YFuLMcK8qnjz7gE3J3/BpV3LdKfgaLbkygsb6PEYXTrE10h7jF2Edo3R2NZjqRAFptwh35qgzIrFwTH1yRKxYHLBzh1ZnlNc3oFZhak0Lq9p2gPe/Z/+gJf+0a+jJzneTWJGMo33K5RSOFVRV01Ic+QFNzsly3bx2pAZi5ccr31Yt8pR+pzKexrrcZlHjaaBeJgWhyXzFVZscJ71BrwnF5je2EacRdCMzBTyCptbTJbhyhp/vKJe1OBgMhWyXDg7a/FLzyXnOXxYoz57k9El4f0fHaNWMBbHlvfsKjhSGe9d3kVd2uLamz/gv97QPHNdMT+c8eSHx0yc5/xhw6W/d5O3v/mY5aOKvAF52HDwuOLFf/KLTPZKHv/4LarDBUXuOXnXMt8XMBnLt1Y897+7xcYvv8j9/+Yx2z4Q1LcQvFhui3DsWp6dgnILsrc1et5w2Tsayait42TRcGtnxPZejlo2eAfGQD6GYgTFpiKbKIrcQiYo5TGZIy9yTL6B5AVSlFhToLSmzYSRE/IRzJdL3KUR5bOXWL5R01YVTeGptZBlBleAZAEgKFOg8lEsbpSDyiHLu3LHmAKVFThlcMqjlCEkyo32ZeVRuYLWU5/PqSdTirxEuRYj4CTkksfokBnR+4HZIAAFZULUgXMumCuyUHxK6VCnw4/G+GrMyZN3sO1jstEmbVGwahswNS1RRR+TzUgKHuzlBxI7kkRLhODRn8LsiMxCIr+KFRo7phbpgSJZd/v4/6d3g1IAAQAASURBVPQ9aCwhORuE9LquU4c7F7wG4lNJRtdwS2jPSW967PML9BFSffhiInWyphQdyuMfsOt3/QxmiyA5a5LxM/EoiXQ1OOAlc4nuKXR6TqKPMdHbWmuSgAuJF6dODTqfAE8Q3pLDtIpgY5CzKND+AaPuDxVJusR5688rldoDF023F2l8erOdM2fqMxfG85ccHxkMYFOeAA0uQ8igNaQyn8QMSgEcRGAgPtYGZ82jcai68NYiWvW/uYjmOvCocdLlu+r5qreseZQk9KN6dBhuGdiTIhOG4YTG59J0iz/MZrTXO8BGgpHgnU22/MSQ0zMYfP8Zr6BbAWkRpkCbOHaCbdo722fy8hde+oXvPvkDuLRxIrSPajvxKctg2C4hRTD4+Nd6H82N0qH6ZAoRodMc4BRaKRrV4q3hZNZQNUeUtsWJZ3l+xL0fn/HUxz6D29hlvDfBqpJqvqQ5OmK1qjG+ABzW54wv7aHLCbQPaVcnKOMopwWzFRQ7mpGMWZ6f0pYtJhtjVwq3WlAdvMH3/2+P+Ng//N+Q397EZxmq9fhG43wNCrJRqJvQNjXtokZPKtRGgUMzlgbJx+RZRluATEqyZQ3zFbrOqN0cpz3QoK3Dth7UitaBci21NdS1ItucoIwlG2VUc8Mo86zqKa45Zn7iaZc1ZuyYGlgtLX4mjJ1gporGeuplzdI7spXHlS1TgpPh3HsuG8+zT2/wm//4y9x5Y8bx64/5zLMKPnsJ/dJlHvzxA7bPKqYbe5wdaFYnKxZKUyjPxGqqvMC8dANjzmmOVtRPWpbHllVtWM0BvUQ7hfnuEc++dJ3fEeEtPO+J5h0v/NaWsLM74pd+9SnGt67hp1MET3XngCffuMvhO084P9G0Jw3t/pyXPvE07957lXGmKTc8ZuxQm4p81JLnYMYZaipkkwlGjwGHzgWdFaiyJMtz0HnIFWEURltyJ1if005ydj55hf0HDp2vECw+V4g4xBm8KqGcoLMc0Tne5KBCuWOMQXT47o2gMx080nUWJEnvo9bToY3gtVAvWpbHx5R7O9TtiFwKvG1ANYBBK4XXDrLAQNpYBdP6BusN0ESalmNoqBy0TGhPV9TVExq3oBhPUVnOKsupK8OpKKx30YkvAgzpQwpV8ryPvzkJYYdJXPDRkUxFDUDQBOiOMVhCVJRoieGA0TreMYwoAAyd2pKAsqZO96ACE0ZArEJSxlIVhcZE35KQoyJzuxCW3Rcyiny4I2v9MxK9TAxbRQDmuv5HP4pBxr0ujXCcoyAACT46pQnRoTFJ2BLCISXxhjXA4nvzcKduF7oShCS6mj5KJ6QhID6kUg/ycwJWdLlchACcgtNjssEkrBEZkIReuS51cTjniPPdo0Oso39/LhlhhoLpzz4+MhiQKEX7zl9g+N5iulmvcMM3rAhMuuPPidl+kFFKXKBpUGFChtcNF8gAqqXfYvynH4CO3hAzXFA/Y4A++jT0u2udySftgUtMdjDO9BITirzY5UF//NpJWb92DWAkOV/639aeOwQiSWU2CB8kOg56G+Z1YEKQaOO66IMo0YSTUG8fsREOh0ecihXEcq7sep7beZHd7U00xxhpqc8OePf7f86tlz6BuXIZc+UG2eqAcpKxOlmwmi1xtSCmoNicIHLG4uQE12rySUG9qtkxU5r5krqqQ1KU3OCXC6xdodsVS3+GXZzw+j//Z9z45V9l73OvwKUSt1jiTsIGqasaaTSuMBRFgfgsBk5oWi/kqkE5g1YjvKrRxmLGmlZKtG0R5yC3VHWDzRqgRZRFMocpW8xegd7KcaIRn2GKHHs+wy9mVMtTdCEUezmqaGgWILnHi0MbTaMtdSOszkborRGrO494+vM3mZ84ZouG68WIyfO7fO5XXkTNV/yP/+wb3Gk8zx96pu88RjYnfPYf3MY9OGZy0vDmn/+Ih6uGSxgeiuV5b6mWcxZvPGD7c5eZH66Y37fUS8PKNtRKsbLQGGEnN2ycnPApY/jDpuXItbw0Lfmlr0x4vLPH//DdJyx/9x7TIuPWUzu88tlb3Pqvvsil95/w6N/+lJPXjpjfPeeZf/IS9775Fnm7REetwGQE2UShNxzFKCfLx5jRCCkn6FGBygxQkOUlXilEZ5g8xxmDdQqRnEpDpnPU7JSdG1c5fXKPPANvgqo81walwrtVokBrTJaD1sFnRBuUCc4EPtNgdGBI2oBKqdRjGKGoED6nhNV8xWJjSSYW6y2ZAq8NVoX36FXQOKhMoU1wEsVn0US1gW0UlhqHIR9pRqMpxwc1dX2O6AzJMnRWMJIR597h3IqmcYiklLTJ4151tGONhijBKqJ2YsDUhhUKJQpoKjrJdcLUwKt9jUSmttZplqSQcZ/0AAPilshx+jJwAu8SluE7Z0RgTc0dmpCu2UAGpdNeJMaXrotydSe4dff2yRQCw4+8Z00LEfmL88lxUhGiySTmaejbDv1kkBI50mvppNE0SDr2u6atTj4T0STu43y43qcizU8cSXzXfqBp6Caknytg3flycDqaRYZGhOS0+FGOj64ZcIlZqgtgIAzFpd8GXpwCeJVQDwwLPXQQMfLWcE9A2hcHGZBy/2tAboOYV3pV2vA+6IV9SKg03uMsa7qULmaHfhzDMXWM90OO9NaGYCDx+p91T4L/3Q0px8HQ4UZ9sF3n4yZPzN11fe7XaYwa8P0i7RM9JljuQgKOBL784L10V6pgm+vm0gMW7w25anl6a4un954mK1bYdkWrLa20NCf3efUbB1x5+uNcu/08o92rZDuecsdSVUsKaTk9DhJ808zIy4JCbdA054xF08xXoXYBHpUb3GKFaypsvaSqzikmY8xYoZpT7n/9dzl+7Q22Xn6FnRdvUWyPwBb4xlG7Fr1qmR08ZvXmI659/pPoTKibDCeKMnOoicUZC6oIVevyCqlMYBLkZNkEZ1womGZq1KrCaij3RpSXNwLxrqE5X2ItUI4ZG81yOUeWcySfQV2hcoeMQn2PlQuFhzaf3qScjnn3m0c8O2145nNjxF6jmE4YZZr5H7/Oa1+7y+GjBe/6jP/5uOXv3QPJ3yO7vEmxMWF5d5+dORwWmnEjzLzmfWWZiuI7//4n/OZzv4a+cZnz751ivGWhNEuEpRbq3ZIxLcdff5fLecav1TWX0bz8LOxMDf/09+/y7mHFdhMSCD16+wmvffcun/r0U3z+73yWZ/7LbdQ//yaLdw4oDhs+9bc+ybv/+ruUUyimQdVvJo7RqEBtjMjLLTJlQHLEjFBFidYlUmSozIRSwlGt7rxCrJAJ+GzEfFGxfXWPys+x7RlZrjDGIKpA8hKd5THaSXcMTESFXBk6/mYMlhBuKEoFicxoMgzahSgDaUGUASesqgaVB4ZhRGhUkJB1XuAzjdMhSkJlPvigNJArjV152tUSNW7JRyMY7QAZbWVRFqwovDGYQmNaT9Mes6pmrKqUCChFC0R1f5IW8f1+jRrXcH1i4oFp9/b+tG8jffTSs/JY+yEJaL2ZsXdm8127CQgMWLSHPltOH8o3xAXr3gYDctfRyCGjTq0PSF26L/YifUoudEqkE/5kDWBIZzpNg0kOeF4GHRVPl2hOPB/MXBiFotTfeK2QBpOEsIvt0/uRDW4NMC+9C0j5E7Tqmb+kkUuSi1XU4ib+MJjQ4dx2c5YiQKLmwasPjOpnHR/dZ8D6vuFOynTg+s0XkgPFRRMTNqiothBP78igL3QvqTricAb8iPUiC72kPPzUv5w0KRGeSvQecH1YxhqKGnDsQT4jhjH8fc7ozirGMJb2L2X2F68Z6siG53wPmKTrV1pMg46l737Y2f6a/r2kMQxv992zlYuLWoI2plMMdmNJCDXF0SZJwgfJw7dMR46nt7fY3M6x/og2Fghq6xrnGrCK99/4Hg/efJ3JZsHW1i7lxghdFFSnJ+xdfha9q1F2DEbR2ApsDr7FeYvKFM57bNViVyuq+Qxva8aTS7S02PYcRMhUQzW7w4Nv3mP/zyfIaIPpjWuojZLWr2gePKZ+fB9dOSZ7l9j57HMUXtH6mqppguNaYRBTU04yfFOSTUeY05LV0SmqLFDliFFb00xXVLZiumyo2WCVWXLv8VVNPikRM2K1WtE0OYXzSLNCmYysbFhZH0wP2pIrYb7SfOyrL/Ho8Jg/fFJx9PWal589Rav7nLeO4gzO3oKqhty2TBS86eG39zV/f+xYvfsYvzdldHWMWhxxGeFVbTkxwtu14lPise8v+L3/z9f4m//bT3N8Cvt/8h7LBs5xzEaaL335OcaqRt++hFNzitcVz1Sa51/a4N0/P2G279nBUEpLTkjmohaed/78fdpH5/zCP/kKN/7hF3ny3/wJJ//mW7z4f/l7VAfHNO/fYbTVUow0Zjwh29xClxopStAGXQbVgcNQ5hltMcJrQesgmVurwBnaFjJxVOMMg2d18IS9Wzc5OhQy7ZEig7LEEbQBSjRiMrTJ4xYQEIXSJkqmQQLUOgsqagVKMrwPjnq0cflbGOXC0ldoU6CMxsckLqKEyjX4tkVrjWsFawUcaC8sTuFw/4Dd61PMaBNVbuL0mMXpnLo+IBNFrhRkI7yM8ZXm9HSfo9OKVRM0DqJSyt4hxZeO/KVsg0ElDogKCktRgaFFRidRpZz+9s6CQwaX6OLAL2ooWPlI+wbAIUm2IROiCyYJR5TEw+kUrdDz8CQdJiARxrQeYTWkQbFfUZBLzfpELxMD7UjqQAqLtF9FP4phjQQ6bUKyyceF0JmnE3iQYWq8OO7Yp2QWGBxDh+0ErCD5h/nOb6HTvnZjlrUx+zW63p9XPgCJNEnDqUrj8LHxLi8DiVt9tOOv4TPQ21ACY5d+UK7vfzTFhGElW0tCbx0fc2G1hBXfM8mUPhi/HsbvBy+km4joCCiq/9UPVFg+dcwNfv+QIyJm30nYacH7+Jjke5sAxZob4frxYcy+Q9G9RD5QV3Tn+8Icw7724X+DXdX7XyS1v6cHPUMklVRSQ6k/vaeIrNuYgli6hd71qk9ulmxw3mNNS9EKN7an3Lp6hbJc0TYzls0pVAppHNpnWGlCBrh2wfnhA47u3cF4wRcaVymu/8bzkCmaJiP3K5QNDj4NnroNWQVt29AuFizPzsgEJBuBBt22KDNCfAYl0Hi0cujcUN68BlMNixnN4TFqdUQ+cpA5Tl/9AeVzVxld2cSvNM6C8Tk4h9EZjgXWCFII+bZGaY1bVdhqTFPPyMoc6govc7JFS7t/QPn005hiEpeOpXAKZbJgS7UTMq9YuZoyFuzyNLSHBU//4qeYfOwp/uX/9QdY7/jhIzhfwtM7wpWrCqs01ZblpLGoU8ULTqOoec/W/PCu8HNKcfZoxvzOOeXtbX7xqzf4+d2GlV2xf6cln804azw/eHDK1f/uh7z81atcvvEib7y1T7ts+exvvMhzV2+RP5rjylOEKewvyJYl02ducfivv4sgFN4xEsXIhcK7o9YxEsvh2yd84//5+3zxf/+fsPkbn2D2tR/y3r/4Ni//w1/m/d8BP7+LbBbk4018DoUagTYYnUc+YPD5iDYTxGSggze5ZDmiNW3laPBkmaKuz1HtinwBrhHK7aswO0WJxoomMzlaZeg8A20QpVEqggAxiDFkWtFCyHWhQ3GhzBg80FqPV4osE1QDbe2oKovkGtd6aixeg4vhhEo8XiXmoTEqi75KDkq4+sJN9NggxRatGKRxzE+PQWp0lqMzkMKAytmfrbh3esD9Y0fldJeLoY/cH9BGNfQTkCjpEjP5RcKrErnpnRvtQAIfZlRco1HQRR+QGHBS83f0ayCIrNGKRK9jOHLEBh/KhYYE5uKpDohcvFFF4cV3kn0KtxM1mKehHsH5ga9ZZJU+8YwAAKI3xkCq74GAdN9jtAF0wKnjAReBzHBg0n8OilePwgUHZ0+X5t0nrUqc16TN+FAG7odzlBwk0zkX71EdOEh5HtyHTfaHHB8dDPiUoztJnBEMdIxWA/3LCr/HZD0xs9XQZt2BMghakc5xxSc0AYSsch3CSMy4QxW9zShCWHrkOmCia7PGz/g8SPCT0OJgsf8sCNB39UI7va6N/sFDyD341x1uuJ7X+xlvkuHP0QbVOc9I/7t07Sa1Ud+D8IzggJPSda4tYt/DnrUpAQwGnTtubE+4srODzk6p6wrxMcOg8ojPQ2pg57CtRZsJ2WaOUR5nNFIpkCxYO0xYtHZuMFKxXFZo0dDUrM5n1LMZRZ6h0LjG0irQ4ym58+Attna4VjO99BTbn/4YvlDUp6cslguUtjDKUK3B24bl4gkHf/ANbv+tX4e9AqoG3/jodJMhMkJLjtMVTlf4kcVrwRuFUoKWEtQCh0IXCvZnrE4fsPnKc7SVIMsKpT1FXWGKTWpnsFbhli2NVFTtisyM2fkbn+Dar3yar/233+D8/Se8fGuTlz5+ib1LDtWcIZWnLSuujg1XgdXK8N6jhuN9xRYZb7QVmwJbCKPPvMDV33iWaeao37vD6XtHXJ1aau8od0ouf+IqD753zPV33mPysRf41M/fYDlbUhzPmT14lby2uFVFfXiKP3TcUy2fvn6d+eUR6mFFKYoMRykw8WBEkSFs5CAnS+78z9/m2V//FMUXXmD+wwfc/da7vPC3f4mDH+3RPn6MkRBd4bMRWVbgdQZGBYitMlplyY0GrfFa4cTQtg6vNUbNqO+fkpUT9M2nWJ6dUBwesvH8bU6qFZlyGK1jimCDj/H/onQ0EeiQQVJnoII6VnRwhNPahGyqzobln2lEgbOWrMhQZUm+mUNbYYyNWgSHoDBqFMIWowSODrTCOWGyZfDG43SJzwo0mrODB9hGUHobldsAHssR1cxz7/Eh7zw+Yf88pA6WjlnRM23V5whQKuQAjIbAGBKXar/Q0cCOucka1RlIa0OmdfH8kD4OrxkSLA8uOeW5gfSQTJs9mFh7jsg6zUxtrtG6IXEUlE9Apu/Lh2UO7HlKzwuS4Bh4RcBsPmadTRrQTtiM/VsjygOa2v3wQRy1BpLSkIbjEvGIc53jok1aiDXtbv8sT3zXLjH5cK2PGguVxEbxPVhJAAHfzZX1nqGr/F92fHQw0CZ0FZMLdSWKE8MNnrC+S1QTF5X42PW+Q31mvPh2EhDoXvbaCgQEJdEJJSUviq6YIZlO4to9YAjfk3oh2VsSQEig5meAAhFw0pk2ulCb2Jf++b73ZYiovF/HwxfQ3y+x6MnwfB9dIb2EDn1ugBQ14X2Y36GkHyMJhhmn8ImNry+0TuXmPYLus215outB6GcIeUwgL8YkR09WcZaJUVyabDAtNY00WFq8dVjfggProtMiLTpzaDEoWrQxiChaLygtWN+GGOl2iRao6wbvWjLtOTo9oVlVZFmJuBq8ZTQe0eYOsRbTZli/QiuDvnmbG7/6FVb+lPZkjq0tBg0F+JmD3CAmwzVL5g/e4J3fXvLsf/6fYi5thvjzlaX1FUY82gYmEXRaNkiQeUltl/j2HOs1frzH6PIWj//4J6iTH0OzYOvLP0ebQT1vUecz3PkR4kbo1uDGBp217FzfY/q5V1AbI370z77LT77xBj//xRt8/NduoJcL/NFj3nxvxZt3LAdnLc46CqO5stfyxV+9xnK/5s7XDyla+EM8P/cLt/mH/8UXUH7F6Xdf461vPyA7XTFtNCvdcPLQ8NJXVjzzC9eoHh3h7t+hur+iWtTMnUWdFoz3NNiK1QPPXDTfWlT82knF9a++QvHPf0ghipFzlMAIyPAUY0e55RntetqzJ8wfPGLy6ZfY8RXf/u3vMvKK5//mF5k9OaF9fIxfzmnyFqczJC9QZY5zGUoLxowQEZbWgjKI10hR4B8dcv6t79A8eURxfQe5eRPz3NPYx49ZHs6ZPH2b87d+yrQYYb2gM4UogyjBSEgCFMpsazAS4vIlAFaldcgToARUhsmzUHiotRiZUuUN5Y4CK3jrUaMRHotSFhu3mZaMpGmz2OhDrGgUmPEW5GNUkzN7/5BHd99jtL2F+DFW12T5FOyEs4OKNx495s39OTOb4wiCj5MQtkhU6zvve3Op11ET0Pv7JEfDLtRwQHlQvWQvSZvQ0bGooU1q2I6+DuhWR1NcT+e60z3D/gC9G9Cd/lGJ3rv1qLg1ehboa88nO5fBgSATbf3050OTvRN4L1wm/4twrZKQ/c/jYyRhECrQSXPXU84YvxGketG9YlUFECkQ+tFpviXwQSFGc4QaDz2I0ZGvhH6E4lc+pOTBhygXD3gVUv/7IMyFeUgJ/HxMwZzm14fkSkLXD+uD/0OEQDFK4q8+/hoOhJHx+WhbSgy9KyOZ3D3i4uo+yiDs78IiSfaThMaG6qvO0SNiuyGzu8DokhqmW7yJGQ+YXw8EhkdacQmlpUemez6sufh7TMK09kw7RMIDJNm1ldpIePUCGk5MvfsphQLSz8HgXKor0I3xA3+Hj76wICS0Fdw1ei/klH04hLYmsiEJNuDQTDLFNB+HdLDO9jpBG72ynUWUQySEAhkVlpkyfaLaxrYYCSlbcQpRcxo7x5iMw/f28U1LZgSVC0qNMMaQtx5Pi1EEL26b0RYb3Pz8Z1C5Rp+HKAJ8qELnHYjRKF1C67DNEoxl/vAO7/z3v8OVr/4ymy8+i5oqsApbj2nlFCTHKIP1OW62oGpr0JuozW22Lo+wtuL4u9+klkf4kfDav/sm+Z/c4davfIrNjz+DvnEDpa/ivSAtXPIhJNFV/v/H2p8/W5Jk953Y5/gScZe3ZWZlZdbeS/XeaKLRAAECGJAEMSA1o6GGxhmbESmTZmRGyWQm/aaf9Q/IpF/GbCQzjX4QKZNMwwEHIAckQQ6aIJZuNIDe0NXVXV1VXVtm5Z751rtEuPvRD+4eEfdVAeg24y3Leu/dGzfCw8P9nO8553vO4cl33+Y7v/6vufv9R3zmC1f5hf/iZ1m98S6Pbj3gD37nmOPHPQaP1ciaxBOU11bK107v8ff+F5/hZTPnh797i49c2+cX/u5PY/aEO7/9ff6f//h1vnqe8BL5TIS/3AjPLVfc+gPLp/7ey7z5rWPabz0iOUNjBN2f061WnD1UFssGFok/2iiPovCV33mXX/kHf4XXvn2P9at3MWrwGIw4Dg8T/gr4Bfilpb0SOL97i72Pf4rm4y9zZf8ef/gPv0LarnnxV7/I/NmbbILSnp0RtgFBiKlH3IzQKGY2o4+BWczpc/3xBQ9ffZ3z7/6Adf+Qxkfk4RlG1/Dsy9hnP0Y4OYOt48ZHf4rt6W1Ma/Czg2xFuVxzwrqG5D3a2ALUHSqCK3F1NQbjHBCIIaBGcHbGwzsXHLx0xPzwgAfv3OapfQsSQJX+PIBtwRm0Czm2X7wQ0ShuMcMf7BODx6wdJ3fv8+a3v4WdRbzuM/czkhqi98SLlrfuvslrt+9y74kjmlyvIghgSvEfpGRZFYOp/MtOS4NFhlS7WGTfIF4GOTpV3kUYyEQm1X8fMB4nsvRDPsobmvGChkn6+FR2T156SYYN5yvARHbHMfYOGJIGyfJeRv0weERk53sVBFCKE9XwiBZjLx8yyv78uQ5ZCVq8LzUEO7DEqrz8gEuFHMYotpuYamjJYJxlfT3K5krsjEO212Q+tdyBjGw1rTpCxgdtynzkoy6R0CnA6UPG+mGvH8MzUBFg6ftdEVWNMxkyQ78UbhhT9erCYKKgJ69U4v52TLUAHUmFKaPinCKSq0hRkWCZ2KIr2VGC9XMu/Usw8lQnC4jRMmZyqtEFMyHfDFkGk8vsAJUPeZWHPe6FcUxDhoPqpXoMaffYKYLeAQJ1Lsnvp/p5+TcQKCv8mFyj/lWNgGEfF+tDdeguBjlfeWaFmbGI9GjcoiERukjo87WEQgZzBclK6UtgckVKMQ19CMwAUl8AxRqh48njDX7uwCsSLM5arMl1K5KNWA2YCMkkiAdc+8jnsFcPid2KtDojdR26jaTQ47xg7Bz6QNddZNzZGiyB9ckPeec37nLl5c9y7fNfYPnis8hhiwvXkO2WLiSsj3D4NEvjQDd05xecvvUOJ9//BtuH79FdQPdIOT8R1rfu8N633mXv+j7XP/scV1/cZ3FwQHd6webJhu7+BSfvnPD47kNEEwfLGT/393+Bbr3m8a17fPd3TvGPA1exrDV3iG/Lpm+ipTvu+Mr/+ID/7D//NNe//h7u2SOuX5+THp/wW//yFnfOIs+rZUPiewgPO+Xf7y0HqwuO7myJn3uO333tEc8YYU+hf7AhNYoPhocPE7cbYRUSLyN8+5tv8QtvfZK/8T//af71/+XLzB93WCKLWcfsWsJfMSyXEbcnuL1DvLWsH7/P7OazLK/u498855X/z9c5e/0+N//aT3D1089hj/bwrSv8ogDiSF1Ht9oS7q85f+8uF/fvcXHrDunkMbEJ2Fah8XQa8OeP4L5BnME/8xwxKCvX4mcG252hbobVgAAhSuYQmCbbF2SFaiVXTI2qILlEujYzZOZxoUW84cpnZvjFEd1xzywKrldS1FxeuJmR1BKTElOXyY4SMfOWdrlAFku2vSOd95zdep83Xv0uMZyzaBZIvyW2e7kk9rbl/v1jvnP7bX74cMtZaDCmI6aEOrsTw1YxMCj8sk+rF7EoJaWIW6ZEv8kGr3yBnb1vdo+hHDfUcq/v7YiL3deQKD9+f8gcGwys8edwmksgQWRUyHk8Y3bC+L2q0CcGUuUzTA1QalZAVaAVFJXbEh0i3AAqyjR/baJnh7kVY4axVQu8PoGRzTb1XlRSe+FjkRtdYYA4SbPMBRMwQ2i9zkgqa3bMI6l1CGrSZc2pq94QJdeeyI8kezlSmYUfzS/wYxEIhxkaFYYZkdt4IwqU1pFocZ9J9n9MiwTVQ4f4P5NFoiPaq58PX63RMtnV+8P5yjFVMcpUcaZLC72iXynZeDqeSDUDnwR19Yz5+maKRSanGxdHHlcZ49TLMGK28ToUxV+uPfArGJf0eHxihw8xfSADuhw5A5VoM+wzRqrNsHBVShpiHs8UIo1GRBm3UVBL0p6QAlYDKXWEEFAFayU3DLIW5xxGMlkLmwCH0Qa1nigRjMekSAwBUuDs0RpJIXeaMy3OC84aSAFrlB4ZXGQ2ecz+8+y9/CJBTgkXK1SE1Pek7RoxFuvnpM16mHXnHUqPZpVA6I958trXOf3u91k8/QL7P/k5Dj/zPPP9JYt2Rm8i8mRNtz1nff8OT978AfroHro5QV0GpkHAz01uaWuF1cML3vztH/DOXLh6reXG0xvWT5S4EfSxchiFM+N49mc+wt6NA45fvc291x6z1wTk0LLZRNgmTMrFcUqyGVuE7Tv3EfezXHl+n8VTDfuHc9740/d4/f4F1xE22qNi2NPEY+C2KLNOeO/uGZ/74rN8TYSvh8BShX1NzKKlNQkrcK1X9kXZYri97vmjf/Z9/mf/x1/gb/yXX+Kr/9VX2LPQ7iuLZYtZhlw74MDlXg9HT5HYkkLCLPdI0mM6z/0/fIsn373Pwc0DDl84JN64gp0vafYt69MN8dEJ67sP6U8v6MM5SQKuEeZtwriE8UKQiLFlna7O6e6+jcHBM8+ybjx+7wWsbuDkHqI5FOBnFhqyta2CGIc6g20bjPWIOMQ3RBFEPb0IpvUkEqZpwTg2x48yFcAskGQx2iGtAetofJuLA5lcx4DZjNR4+j5gO3j01h3eff07SNwgpi2ZNisS+0iEi0fKG7fe4NU7p9w5FYIJOOOwlB4LWsG4UD0Co6VvoPAe4ih6htLEoya7LFMvv6rcGJXouO2rkikypQqLQdwUWVo/33lNOAGXlZyMx0zJeHm4Ezn2oWPNsnzK7t/FPZnPkQHRBJAgYyG6S/K53lQi9wGQHTk9Drza2SOwpHQsZMzQqqpEB2iykzIvqYZGMqDRMnf1Huo0pgKmtNzPmF2WymNIxVthhiFqAR1Jsye2aN4JxPnRXj8yGDBqhkpdQ4nLwc0/ydmvbYrlEodxKGajozdhYp1/eMnJerOQ09xkeF8nD27gGwxfYAIA6rWn3oDyb7pxJut7+E45Xf4nWdhMLfxpKGB4gDI86GFck325U02w/D5Fl4NLC6gFJIaGF1pi9zXkMQEuUu539JxosdILgbC6yxJoKmlBqkjK5x35q6PDaXcx5b+MCKtgON2u2Wwjfg4h9YDgfHYnIxZjHd43uZ+2MUQNONsw8wtCnxWziKChQ3TNk/sbYheZzRsaPNiINQmx2QUak0KyGFPCG5sZVz7yEvaqJT3ZYgjEGIhdz6xpUS906zNiilgU1wihE0QdxirBWuy6I4YNnVnT3z/m0a9/F/vPWpw/yAVm1oanvvg8ds8hF2uUc3QGulX8VjmJ0AuIicyXhm0TkXnCrRr6C3jvduDdM+UTN1r2F7BdbXG9Mmst17/wItt0wdnDE0hb5EZgvs6pe3Zl4UKhVxoFo4mg0HeR89WW5rk9jN3HeeHdu3c5CooTwwJBRJkXQPkAww0D9x+d8tNHH+fzrcVdQG9hGYVDgTmCE6VZCHtiOA+RpzvLD7/1Dt/5/+3zxf/yS7Rq+cF//zWWdktztac9tMzmM0y7wC2WJOuxbkbCoD6yQfHSY6Lgzs/pXjvj8eN73A8N202PtXldN3PFzcHNgEZx5Lz9zmhucRwT3uV9k+vlG1zf0T94B2nnpKOnWM2Uo71rzJ7a5+LuXdhs8Y2H1mAbTxKLcQ1qLWpsHqNKKY5mUAsxWcwcZLbAJ8fZm7d4cus2Zl9YhciVwyOWi0M2khAHeE+yDt8sUDEksYRtwKwTj96+xeuvfJXGG4xpMT7H8wNKky5I/RXevnuXb731Pt97v+MiGjAhK39jckpe0SimAIFcELQqv4mVLzCWc5/IskHGjsd/8FWVvI6ycCC1weA9HSsIjdru8jl29OslA+XyT2GUX1XZ7sjuycl2LK3JzwlAmRouYwt4stEyUcY755meq/yRQwT5z+ITRTDZ2i5zUh0hVd4azZ7SHLmRokZKN8OaFVeNl+F+ZAxra66WE0sRu3GmJnNOtfsZdNIQpJAKHiYSOo1daWNZJH/WCrj8+tEbFfUFnUjlMRb8MyygyCSvZUBTI6dgukCmT2PQ7uPFpCrv+lJq2oq4shmmVvzkx+g9GN+oLpfda9frkBV99UKkMvYBIAg73oTqDhj4AjIuThhj88PtjJ9PAUS2/vN9qrn0uLQCqemiSLv3u3ORuiDqfVdSYQYIwuXvyfBnTjGE3MQjC9y8MZTaOSpzXTJitVhOt5EH5xtOTjfM9wSMo2k9tnwuxmGsxdqMbFMSrD/AWcVK4PjslKe6RAodbM9ZHZ8Qt3B0dQ/U42rLtJQIKeQOhyZiUkK1IfQJP7/G4toRYXMKXUQidOcrnAq29WzXW5zGzL51mXxjzIzQlSIcSUuJDEsIEZlF/CKg28j2fEXHPp/8W/8e7TN7rO/fw3nLdp1go2zbGRu7ZtYoZpZI3tB3CR8Fr0IXe5okyNaQToT3tOMnPm7Y84B36NGSp56/SttZNqd32L+hzDth2yjNTKCJuCMhBOh6A+dK7A0GpTGJ2CU2e4loPX7dcd2AEyFiwSTOsXhNuKi8mZTnsFg/4+krwoMLZR08z9qAS3DQOtxLhsMX9uhj4sp5x+pOx+xE+da/+Db7R0s++Xe+yLWXr3PrD75FfHSHhg5nFli7ALfAG0sfW6wLzJqccZL3W08XLb54Fa6mwJOZIq7Hi8M4YBFxAmqgdUJoyL0ESIN71jqHNA3J5qIxtu+ID2/RtnN653i0jizPHYcf+yTarenPLkghYCgxfQwaLGqFAKUbZf5H62nMPmYR4HzLo298nyff+y5p4QlhiSr08YL55gqHT92kmXtM09JLznwgJLTbsn5yzMN33ubh+29hjKIpexKtd3jfIjiIjtR7vv/uG/zReysebnPRoxy4ymsRUaqZOa17VuWTipaQQCE6D4j9z1D+1U1/yUzcyd6CD1jqO8ZV/fmB9y6/xvd3ur1eOiYbJrsy+sOOq/WJh+EMdtzl46uBZCYH13uryt5MOGsMBmlWzLKrUBHswFXLxwrsADXRXOxZBl1VJXb+jiml3mt9liH1b+KBzQZe1QUjYTGnBJbxDFOU583Ux6m5vNCwPsqxhoQRMklSM6AxkwZOf97rxyIQ1gpX9QHk8NKUZTpZXBrJMLouxFhGnF05g0tneo3BbV+UramzYYfJyz/MzgO4DAqk1ByvivGDzPqyqqpFrDro0YE3oOOQh8oGU9dbml53ChbKGGR6bxOUOtjZOoYZSmZFde1ozcKYuP0rIBj7K9TTFU5AyVsdjqsLl3pfdW7KreULDS4rQTLnAyGS5XF2U2UQYsTk+uZY+tBx3K857lc8JR7fgo8gUXDkjIGMlvMF1Sht4xENdP2azfkF4XyFDQ0XJ+eE0xlPPb3AeiH1ELaRlPpMtjGCSsqFjFJCkyNFR3P1BohieqWPkYuTM2IHrfcIXfYqGIdZGCT0xD4gVjEWYq/YmcdKQ+gucM5m6opGNCXsYp8X/9pf4eDl57m4fx9nLMEkfNPS9T1GL7AzkBBJGGKfcHNIwdCvEn5P6a2iM6XZOMLM4K+3hIcXGGD57CH26Yb18TnGJZoDwXaGZCLRwl7r6GIgBaHdgLGG1ChEx/5ew62TCy5sRLXF65wrC7DzSBIlOmEWYNY7bAz06njWz1Gd0T7lOLgd8aZj4ZVrzzbsffIIvXbARfLM2hnzRtj/Qs/8nTuYPz7h9//bP6Ql8ex/8BO89B//Kv1b73J+6z2683Wpi96Q5kvM3h7ORPTsIS8dwXsnwlwMPipBlbhRzDxgG0HbApqNZJPJCOIgeMAmnM+1PbApdz2SFiMONRaalq31eA30x/cwxsHBktWDcx795ls88+99lr2XXmDbXSCrhIYEfciph63LKaLWQ9OQWg/e42Pk/PU73P/KNzm59Rp+HmkX1yHO8jp1LWG95sG777M4OGC+tw+2petP6ddnnNx/wNmTB/TrE6xLmMYRt0pIAaLBpTb3YEkGIjx6dMHt0y3Wuezxqta+NSgxFx7KQiKLDZuVnDE2h5kH+0SG/T3I2aky/DCdPXgWdCK2pvJkcuxl1/0gX2qUfSpbi3ymKksdvzMo63yvY4bURDDV6w1j0oqCJl7j6T2NHLMhrbIKuMEQNSOwMhMVVT3Tg9FaNIXJzYXq1FYPbc5eUJLKUB7HlPFXUiODUp9E6Yd7z/osFZ1ZAcFQzEkovQryAA0MpV2AETBMNYpUfVQKVGkFBKkUJwJbOQSTegt/3utHBwPDKOqVi/IQqZqjzmBR4lOLeYLS6gOmrssPQbLlQY0LYjK5094HOy4lhjWQ3eDkhzIswkvnL0hyN65vxzGW0PwQy9LpPdTvX5qXyb+BDPmhC7lsoUmoY0zfGzfgEBaoG206lmF+xnCIxpjJOJfOPaDeGuKYACOpoxFht3lTSb2J4+YVMnEvmsRpB+e9Rdwhrj1BtmtMbxA1WAOm1pYwDpEW7y3d9ozVxRqCYf3kId3ZPhfnW65ev4JrG4Lt6FNANdKHHkkRNBC6bckHz7QYY6+wd+0pkIDGSLdZo33PfLbApJ5+dYFqomk9kR5FaKQldBuczwohSF82d8huwSik2NFJ5OiFT/DM5z/B5skJmgJqoJnNCKHLrltrMW2bN9u8y811OqW7UGKvSOswRrEJkgdaR3v1Cv1Bxyb1HH30KviG1K9YLBakheG8WzOzCWkEWSdMBGccfZNoFkLSiG+OmM3hvffXWP8E0glXrrSsDpTmKSEYw0WKzPrELORcZr9J3Hz+gLhds3RzFrrGXd/jpS/tc3x+wW+8tuLR8RkxJubOcrU1fOaTz/HpL36aL3xixfG/eJ3f+Mdf4xdev8/H//ZP0X78oxx96uXcK+JkSycCccv5G/e5/c3bHDy3ZPH0gub1Da9+9xHPaRZvHnA2CycHxFm2/BtfBJeH3E9IwGou3EQA74jWYsTgncvrybpMKu22cPKE2DQsXnyOzQ8f8Sf/1a9x85d/io/9/F/CP3NE7xJsOlKMtOKwbUNwud16WHdcvPM+j/7oTzl5+/vEdIZdGJK3XJwes9fsY53l4nhL3De0M8f56Rmnx/d5cO99LAk0YKRBQ4d1hlD2pW0NqU/EFEldLCB2jegMwoKkx4PuHrrlCWjpYpodn3mtZSWVCw4NzsxBRk5eZuT4jCTnqiUuHXtJeA3Mff1zjhuMi2K2CB+Ug8Pnu/JuRw8MdmM5YNIYbbzBHVTyIb/LINPHIU/koqmZGGWeSsjFDMfLcJpBb5DVy2jy6s68VTlqymdaOAzZdqvAoNxDSR/XMh9TXuaAESQHlpOOhZCrsTaK+DETgcn38nm02Mv506nK1eHgHzWX4MciEKYhjTNb/MX6Hdz/Nc/k0qUlMfo2TFa4Q6rMJN41gIzhi1Tuwa6rqypAJqugzkBV2ruTsbOYJlyACgSUPKHDoVEvcfRGMCJKLs08XZ+XF/4w/kvXnijhnZ/TsEDM92eMkGIFMpMVMny1LLyBUAiZuJm/ktLOAIchDuC+otQy1jEF5dLYJPd9r4ss4QgSWG2VbpsQljRNdtMbAZdaRBzWOrCGGMFbD3Ssztf0qy3OwoO79zj42JLFjZtoY9GNh94j4YK+u6Bfd8RuCyS8ZFeX8TPUdrQHV3HLltB3EAJpu6F1Hg2Jrt8gXpg5j2x6RCwintRB086JoUeJNI0hiCW1JhdGCiC9gyg89VOfJ3lLilvmvqGXgG6z8jLe0izmWCds12csmyVhvWXbX9DMNBd9bPPc2AslzsEZi20PafYeglPsFU9KlpR6/MEcDQ373rGOK8R2OB8IQRAie/vQS8KuPcsXXuLxrQvuPIk832xYvR+49smn2b5hME3mehyJ5fGZpe0SLhl06Xn+08/j751xchGYvTjn2l99nld/7z0e3+542AeSTfm5hsRdG3hy73XefuU2P/WrL/DX/lc/xb/9je/xyh+9yf0/fZuP/+wNwpWbxKah6zKp8cHb73PyeE1KyrWbDR/568/w4t/5PMvPPOb2v3wDc3pBZ4SDwxZ/EhAHTWvQ3D8oFwTygrXZCyLWYnyDMQ29ddi2xRjLZr2lMTPMwtNbxWHQuMKfnHA622f5cy8wv/cat7/8rzj+na+yfOkZ9l7+CPs3nkYWC4wK69WKsNqwvf+Y81vvs9rcxbYG3VNIDhcCkUx6XZ0/ZNE6GhZsL87BGub7BywPrmCt4fT++ziEXh3JWzT1QCLGkIlcxhFRgq5wPQQ1OCO54yEOIYPm3IK4yBdTLdZCGSsdXdXYAoZHUahViU+3uRbXdRFBuy71UT5OUMNE4V1WGx9y/I5hl8bf6zHK4Dof3OEfkIPsyOr89TReXrg0lKlRVd/S3eOnyr2MpWYPjFVlZXLe6flk53qVySWlpvI4vNzGWixjQ7yqt8jtg/PBsdh0hfA4UU8D4RAG3kCVumP6Yv0tr4JU8Uy9VmIAIWYSjpk26at9aaYS/S96/RgVCKsLe5yu0V3ExNqsk16X89TaNNWpQZ394VtDDOvyIq1KD4ZCQVNy4GAh796yVBfW1N1OBSTjsYOxXQdTuAg7FIfh4PL5pE3nBw/4sKmfjmFyTPVSTDZNjaWNZQEm96Yj6qziQrW23lSk9INIsWyApCWMVndXza4AU3kSNf1FcnxVK9eg4NGRh1TdUdmjsu0i3XYL24b50R4yNxBAt5JTuozLmE8DkOjWG/p1T+jAxkS/OWNzbrny3CFpvaHTJ+haiBePiauO1GUrXFzOEzfW4lwkSsPsylWSV8Lmgni2QXuD0pNSh28M3rXZvdtU9lluUJNCwPkWkxJxu8b4gGiD9lu0C6QY2Lv6LItnbxD6Na4xEEq7Z2NQIzk33TRIazEzj0mBmHqapSduA41R+pDDESJC7ASCkqJFWsPcWGK/xW4DLhnagyvEviOtDG1yJL8iNT1CLqcbUwepReaRg09d59XffpP9LmKenLF+5zHLzz7N3gvP0Ghkw5qD1mFXG/rTQH8amR/cZO/aHv03fkiXIld+9ll+50+Pie9ucEbZU0tf0v1mIviozIxDjre8/ls/QET4pf/s53n1N79B+/b7vP3Ne9x/fAevwtpEelVULUsMXhMXt3ve+ee34VdarvzMR7FX59z+9e8i245madgLjrUGjM/8H+stbgaiuYiLKdUIjXWYpqF1GQw454m2sPeTYJ3N0cPWkGzAnp8RF4dc/Ym/RNiuSHbD6ZM3efJ776BJsdahMUHMxNTUJtJcmB8siKWcsRFLNB0mJaxtiZLXiZU5WEvo1oSwZNMHFk9dRRXOHzzILmPbYRqLE5uBXoyoF7yz9H5LzxM8LV2wnHRbok2QLENR1xxXg0pYm2g6KbFwpX7OqL/q35dVytSoGoylUSINf172GAwf/BkqZBDRlwXn5KKqg6IbjZnLcm+U7zv24DRkUePxEwU+6IkaDhhAwWRS6lzW4wWKc580BR07Vx6/O/Wq5mHk9L/KF8hOlyJrNWVyoFBKDE8AxHCv+SlK8QBUIFdvI40WWjlyzCSYZj8kzZyBIbW/zp8yei1kbEY1rogf7fWjg4ESe8mx7lgWZyYojOmFZnRz17fqsCZxmhEGy2S0kyErE21ckV0BBYNyrBeYoKHhz1S4DB90YckOEJgu4lEhjx6HEbAIk4eYv3zpzDXzs47NDN/Oi2s6luIJGJ5aGs6xw96F3e8NGREF6KRdz8Bw/VK7ASlQTGBoD10UfP03lpdWxjU2ZMp+oK6SlQ5jDJsezrcRti2tONQnMIlee9BEIub4LIGw7dlebOhXHYScliiibFZr1lvD7DSg8YwUoY8d2gba1pYxlrSdMn7hEDNbYhaKfXJO3K6wGIIkjE00jSN2G1rvQUs1LhGcs8QQc9wuRqwVUrcibLtchdDm9XD4/Is0iwX6+JyIQU3CN57QR+xyTvQQgqFtPKHr0M2WfhOxc4f4Dgk9ut1irKGPGZglE0kk/NU9WHe0H3+e5POGn88O6cw5qgZDdolLm4m6CcPMWkyXSFeexain/+5dPrnfYGZb1t+9zZWfepqnPvoxuu0W150SIyyXPSwecn6x5tmf+Tz9+2vWd+7jPnqNe27G5vvvkDTQB8NSIikpmf6rGANWA74RFtZx+9/+gKNnr/Dyf/STfPcfbri4/YSZJHwSZqqsMaxFS8U/WIjCBm797tvItYarP/k52r0ld/7RH+DEYg4tfhXopWdhJIcEfCbRWZv/xSQ5t945Wj9DMah1zK5cJfYhewS2EZ3nXvFRAs5s2Jy3tNdv0M6u0ZsHmLli9mYIG7KrNncWBAMmVw6M24CTWbbAGw+0uQiRCq5p85o3iRgUY4V+e4FbeMxsj8WN62z7FdvTE5yVHNowAuRKhUmUZBNm3rLF0fnA49uPuXN2zgZDI0M0sqz1CgTIXqhB50nB9hOldVn6VO9hfU0t4AroK1BQ5QPSS/8suVZeaSp/p0q0KiUK96kWTpsaRpf+rgqyiP8dTsDwb1e27+oIHd8bOjbWualk8BrNN5eOqecz4wB2rq7DFOt0jDKeRqusZEzUFhhKHRut9QpqmCDrTSk64nKiw65PPesgMwEtNQwxTHe9ok7njtFLdCk0/Wc80Q+8fnQwMFyoEH+Gh1UVdl0sto6eIbugtj2uKK2itlJUYQCRuzqYAQAMbMjJgqw3PayPaW7p5Jgp8tViHdf3h4uW92sVRVG0tluuFaTK8bsTO3kQw5OqSC2O49E4uY5OjodaSGN4qGlcSANImKZlasg/S0xqXN+6szDGxhU15j9OrQ5jl8GJMyyfCpCxw1zV47PHrQEiKw3cP93w+MkZN2/ewM8tttmiuiYFzRU+NbPsL45P2Z6dY1VIbIlphTEt3fljGo5Qc06KBiTQzA8I6Zx+2yGacGIIsWLM3IjGNgbjYbvp0NQRQkRNYr5oiBqYLxeEcAElM8KoEmKXq9MlxRpLvwpoFKyxxE3H5mRFuIgcvPQcIYSMzpPJoCEIxpeyxCbgjM98AxXidouftfjlgs16TdpuceIIaYNtE62xhNQTTY+f7UO4oF0sUQmYhYE0R7XDiWDVkuycFHtMA8Z6SJ5ellz9wud569f+EHl0wX0riI2YH/6Qmyefw338JeT9WzSFfGm3K/rmea7+4hHtvCV8703OXODaz32Mr/2jb3NlGwlqicawNT0zHCRLr4GFCljYO4rIPLKYwaO33+DmSy/yqf/kp3nt//rbaNdDIxDyvr4IiUYsRiIyM6hNWO24+2/ewh1c4cqnX8L9p1/i4huvs3fN8+DknIXLa1oaxfoFpnQyVTE477DWI80McQ5jTGlA5PGzhiAJvYg5jh57TDI4An3cIPMZ+598mbuvPsS3FisB22aPZEigM8GKI0bFGkfUFcEozs4wviWZDmlnaOhJ1hclvMGZOdtth3Ge7uwUNS1utsfhMy9xwltof5rhfxIQhzcWdRBMLtplZnuEuOB793/Ie+seUUs0xYqrQKBYj1FGA6smF/RkgJ+K0tuRQxO5OW1IlM3OUdb9WXp+ygX4c7kFg3ydKvUiz7QS59LEkBt/7IKDSx8Bu3HZS0cMIY0JGNixaNgFCzthj8HqKcLPTI6Z3mvhLFQcMpwh31cqz2coBSxFdlezSVO57KiAtcRzjIJJmrmy9bzKMO+D+39yzbwyxloPA4FwqifZBQMj47AcouwK97/g9WOAgVoNQ8crqRkfvFJKDNSbkN0HNJQdFgZzUxnTPQaFVRjuCuPDqWih/EwThT5Oy/DWSG5Rxh0wMXGHsZS3BtRVrPthDaYCCuqltbjodytS5a9X4DLNCb0MPEYrXyeLQePE+k+pDE9RGUFERY2jhT9Ff3luUirIU0qBpAIE6qKtC/gDm3JiOQz4VEcOwRCtEUHEkejZJuHWyQVv33ufF164ztOH+1hTgjOyQSTRdz3nZ6dszrclfWGDaulRoMrJO+9z8yPPs//SASm2pPNzYtdhzpc0fkbc5ucv1uZNlxJqZ/i2oduc51i/bUiywTWOFDvaWUPsA81yQYiRWoXRtQ0pBiQp/bbPZMuYMhHNWFrXsne4R3v1gBA2kBRnHM61hLRBWk/sArZtMvM9JMzWoNZjXCCogm8wKI6IphxyMcki64R1IFshrhKxTzTWgPNISMzbJVtdwSKSugYjcxKKcXOSWbD3yY9x8cZDTv/wB8ydcLEOhI3hu4+PefJ//31+9h/8Aub6TcJpz5xH6HKBb4+wtDz65pu88vXbPP2J6xytIrz3hNkSrIIuhNnSYp2wCh2bx7mHwv7C4RfCfB8O9gyNdnSPHrH34vM8/z/9CR795jcxS2iOHLfuJGbHIBJpFhbTWty8R7wi8YLTP36D/aePWPzEZ3HryPbeHZpoMCbA3NE2QNNkhS+C9R5UcrFd57LXpv5Ec5imaWmsySEKm70aXdfRtEu6vmPvEy+y9/BtupMTrMtlpgwWU8ISUSsPRRDZJ6oQTMRZZWYX2ZXsBTGemCIhdbTNDJ9s9iQ5JTanGJvws4ajZ5/n+Pa72LjOjalEMDgsi9IYKSLOsV3v8frjwJMotBLpC9t9UBxFtYjk2gKlUSGJ7NHCXMrAMjIS/yYG1GhYTBRKVaCXres/6/VhyGEqdwb5PCq/XWVetdaooAZZu2PsMZHvw0U+fHxy6Rcp3538vZNCqEJ2+cHQM2DKG6j6hmn9wPH9Og2D9S818joJbZT3kDE1sMrZ8q1BD5kyZ+PUlgyyqae7GIIDINBRx1cwNpben8xvGcwuRaQ+lR8NDfxYnIFxZOPkDATCChaGlTEFBONHOaaSywt/oGXmgCNk/BsBEyfHpFFpD6hHh+/suKgmiEumoYyC4Ec0azKBMOXiSaIuX0Pq52WGa/77ZHKHNL9JqmN21xRFNICOS+k7ZVMMJX6mz2uI24cyzwWgFCCCak7NquMri61aFnUMw2ObWAsmf7iDiSnTPObCmqH9p0z+n59Xj6TMlr+37fnTx/d48f4djq59FH9gETkkhXNIgX5zwWZ9UVpZ98Vboii5vKpplcdvPuDKiz9FOuzBnaObU5J9QtqCNZ64UWLconi8a4kzhzQdYbXCCWzXAd/mHgd+tsiJLSqoScR1j1VBrKMTJfYJtonUJUw0pJTvU1uPbDxu/xrWeWI6RwOIF1LIG9NbIVmLbef0cZ0rAxoLvkGNkCyY9Qa3SvR9j2tnOe7cdVhnSX4G8QnSbYnHx8jzcwgWmoQNM1wTUfU4OgyW6PZI+9fYe+4moV/z4J/+LvN2TboJ3HKsQmBPLK/90ZsEafjZv/uThMWCZHsMifhEuf3t1/neV9/lzfunfO7vfoYnP3zCwZEgc8Uag5tHmiYhM1gAFw1oY1g0gcZZ/DJh9xQ/MxC3bFdw45d/kvD622yPH9EcKJu3PK4NOGuQecQ0YGeCbRLeCBcP7nH8zR9y7Vdu0nzho8RvBtyd+3iXcC4DMSMG6z3JCsbnNEJnHKm6zCX3EcigEDyW0ArOZYJeLi2ccKmn10BKgcULzxE3HWoCRkPpWGjBO6wp3KXi9zUkHAZr5qWE9hZMW6R7Lu6a+ykITjyx64irc7xrSFZgtmB+eA1ZP8YkS882t09GMI1FfYPzDe89XPHag7tsYs4SqOon29OlcqdUxnshsFXFZC5Xz5tu3HH/jgJm8plO5OEA7CfM/emxw/H5uF05URvOTWofTowazGhUfSC+uGPBXpJ39UI735keUMMnY9x+LLY06h8phup4S9PR15L2lys6jsp+qIlTp2Mw7KpNr4hU/VNHKWU2xtRwETBFv0jVjwaIaVTko3k3mR/N1ynzIINekMxHmOhKnawcdOK1MDo+Eq266Ed7/chgQMwwrmEaBi9A7Sk9Wbijsi7W7Y77Kh83xjgm5011ei4hy2q9DwNi/GxHmeolpFkO08m1ikKdptFknWmz/k45hJGmbZgnxEGl1jlgAo7S8AR2KDHDRSfjT+PfQzjg8uYon0v9XtLBIzK6mEaCiYhMvURkGko+1/T9+r/CLRyvVc8k+Zq1p/eUnJJfFkWxRllHx6sPzrnx1pss5zM+/tFnaBZbQoikTc/F2YbQKUIW0LVQlAp41+DcnEdnt3B/uOSFn/kENDNYWCQGUh8wTUIS2GToYySpIM0hfeywMRL6NaD4Foy1ZTMbklHQhF3kcshhG4h9InQJ06dsERqH+JY+bPGNo28T8+s3sK2DNViXG8H0XcQ5Q4gb5ss5QSJxHfKSX3hs26La51r1IWHokWaOLBZ0XcBttoTkoJ2zObmLkxP619/Ff/ImYoU2NXQu0ro9wvmKaAw6O0KevsH8qQM2Dy54/N//a0z3hOYIttuEmVvMueBSxAfl1a//kKvPzfnIzzzPyeqE7Q8e8MpX7/LozilhIxy1lqObV3nvew/ZeypX/TNGsQvAKG6RiXvNHmxPDe0SXBPxjeJmgm8W+GCQFEiH19j70seYv7ni3bfBmi12PxeYsk3E+gDe0vjcSdDayMmbP+DgCx+jfeFF9j8Nm/v32PYPM2kwGpwKzjqSMTSuIYghOktj27JeTAYNrily32J9VubWGJIq1nuUgJXIZtMxv3qd9dFj6M5RdUgMiDVYcbn+v7EYa9GkOJt54slYohWsa6myx5qiBjQiJvcjcLZlGyJdt8XPBJMsbr6PGMWlvpRO8HQIZtFgvGWPa9y/9wrvrzdg90mimCoyJAOOrJjGzKWqdnWa8jtwiorFWT211UL/QKqf7v4+KFwz2foTrb8Tv6/i69I5gCGjq8rbetyHaZ7h+9MQwlRRy+Q8RZbufD5IuaxHph6CgQ9wmTg5+WOg4g/m++7rAyGD6Wdm9xYGJU4Gqzopf6yl0mAZ8UCJ00sh3AJqqnd4gAfVONVUaqsUvVp1lRnPPc5RNcLL6LRiPB30wJ9xZx94/Rh1Bi7FggZFXc35LIAz4swKY3zmUp6jDLH3XdSWRnAIOb1uWi1qcOMnhgZGU17A4H6fLuTpOMtvA9LT8ZDJhtAkQ9vSHVJL0t3LEQvIqe/lk+Q9uZsTukuokRLCqcS/Oo3ZA7ADaNCMJBmVfyWhVAKL1EetNdeVcdVW6796WSa3MBwLo5txMlXZdh/oRqNnglxhsLcditBY4Xxr+cO3H7Dl2/zSasMzLxhmqeP8/AS2AU1bSLkqlnEN1mcugvcOK4rayKNHr2G+Znj6888i+3v4/QVJHhFPe0RXmZGvM9J2jTN7JVYfCVuLm3WIbXLFQ2PQVGrZG1M6EQZSULx6TOzRPpbyn4CxuGZGTBEzX+IOr5K8gU1BSiFgDVgjNLM5yYDtoTEtofU4I4SYaIKHJx0hCWE+w7glKQSs7Uh+wZ5cpVFDd7yFA8f5m+/Rfv0F2s/fwJ2DsStC6tH9azSHVzBHByTxrF+7xdmf/DGz5pj2YwfoRceKC+YLw/JWy+3jns060fQd733vLjeeP2L1r77G7Te2PNhaLIFWYe/qNdzeHtdenLF9BH6WrU47B9M0NIs56hvmVxPr+6fYpQMLMzfHkmjbJoeANJC2iYMvfIr33nqf1eous+sJ4w3qe1wC11jURJKBpgXbKCb2HL/yfZ46vI68cIPZJz+Bf2gI2xX9ZotZR0Qifq8hIXjrETG574P3iMlckYBixYHNxczECNI4RBTxhiQRG3u098RFiz08wF4kLELqt1lSGCkK0aLiMXasTd+0ucrhUHnTmGwEpYhBUKOodCgZtHTbNdIpTWvpZUFSy14jpeKbp20UmbdI4zh/1PDKu++x6hxGJQPWQnaTouBUY1FoJVRQMg1S9SAWA2hkyH+IQv3zXlLt10uHfoAnoLugoIKNpFm+V1k/GcIYyZ3K3nqhoid2vBeTj2tu/oeMdxz4xJofrOuJ93mYj6qPTNWwVAN1ELHDsbsycrdVPQzavNxgYmwfrBQ8VrgEtQOsEcGQMpEQmHqqRzygu/NU6tZILUtc53uY3CyTs9qc6Dy1wylMebZV1ufMhTwfU9L8n/f6kcGAEj6g3HcR3oQ8uKOIi9ujgJvLS1amhzL5vLjBS2J/LqyQJmlvJUSQscU44TkMEQsYydfPD+sSK79mNNSFrQKpjjUvfCMmewd2gED5/sDgp6C7cZNN02qklocqC2AHjFRlO8T3yzzWTVVCEsP56l4o7rg6T3XNG8a0kjQFBZdjSwW1SinDKZMHUNm0dXjjfsyLNGgAPKJdptwIPNzAN+6e4fg2v2o+zeyqkraCCR0SNWcVGI+h9ARP2auk9DR4ZDbjyeYdVq+teO6Zj+Nu7tHMrtLbFf1FhFVEVonYO1JcIf2M1HeItPh2lnlSIeGa3HO+1w6ShWCQ3hA2ATTnCBvviKKYEBEcmiLabcEKzWKPZOxErijOWZqmASuYrqenQ73DGZtL3ko+b28dfrHElOcR1ht0ZtE+0u4viScb4p7NaWirDd/5J3/CZw5+Dl6+Asdz/NFVZkcHhPM197/yOnd+//s8eOUtenqksfjZjMYrdmYJm8jF1vIkKgs1OFHikwvMc9dpnj2gufcQusQ8WqKBdt8hDRw+c53VvWtoWmOM4GaO5BzWNci8BfFYEbZ9x3yxjxWPIREAbwK1hbnbO+TRfUOzrwQHmhKzRfameATf5EwK8TnDIC2A42P6e/fRvSX7n3uZs2+f0ZoNGhL9ZkM6W9PfP6W9dkhzZQneYar1jqAGnGty0ys8QcE7T6cJXM5ISCliUizprHNm+4fE7TnGOLRxdH0PAs42pdRxBrpGs6DXlLDOYkybSRXWZG+UpiwbJCKSSLqFkGjaPeK2Q9secZE+bUkGjGmxxpF8gMbjOOTVt+/ynSdnRHWoTYwV5SroToOCGXTZRBcNoe5B7k7056A4BoG4s9dHxTOt7XL5uHredOm7afxo8H5Or8so18YDRwBBARBTA6zeww4qqR9koDac5DLeGW58Ag4+7Fhg5LdVYrgZT1KN03LtyynvdUwDz6BY8gkyCIW8Nqr0NGbIeKpjqXyDCjt0YrSKKJImvKyJZWqGh62XpjsN4yZBKl53WwmSRV2OmKnquX/HYGDXvVIWmGphuJSfU3dRHTQyIq5EoYWPCE+ZDD4VhFjXhKQRcmrNwa8LKx87tlMeme+VfDG0stxBrDL+G5Q2o/UfdWDqp3qdgSVSr5Co+aQVCHxwkxQ0GHV3Y0yAQr5uvuZYvEkne0Un12XwCFilCK96PgalL+U4pCr6stwvbRRJJdY0eX8UPLs1H6bRyl7Ak8j11BNiE8Z4LtaJe+cN8XTNZrkhdVuUiPEeRymOUdxlxlvECpgGYyxtE1EPGo659d432D/9CIfXr9EeLLCtw8hjcD0qDRoicdMRQ8SYHoPF+RZNoVQIVJzxhJDo+gAiuKaBvqPfbJGYcPOWaGOuZpcMNiai9rmOvbGlyUgGLa51Oe6batzYZVGVwLWOELZsY48sZzgMXKyhDzjfIHT0vcHvH9E9voc5aIincP84cfH4mFf/m99j/1PXePpjLzB71nH/zUfc/9P3Obu7Yhu3GElIhHTacy6RnlCAqdIXL9xeK9ilx4Qt4htmz77E5k/vs1DLTBIbr8RtxKY5aX7I0XPPszq5jwh4L6hvEPFok7s8+pkn3H+INQ2m9TSuRbt1ludSIsYqOJeL88z3DfiiO+cJlyK2GCzWG4xNRCdYo2we32V+/Tr26JC9j32C+Ojd7Akg4K8E+rMVvQFM7m/hvMvC1liCCOoMEcU7m6s7qsE5D1YIXY9YS7JA3KLdHOtmmdQpQiTSugaszb0JqIaCIYUeqwrOFM6ZxbpcV8KWwjOaLDEFQohYlwibgDjFBIibDW5/TuvmKGcYO0elhCXsPk8eKL/96qu8eRZJ3hKJSHIo1Wgxo3dAKFXwJjaDJCrPKm9IGcXMjuH1Ia9BmVfFLAPIH5qa7QiAqdE0yrUdFVnfH2TPZLA1/UEV6vnLOLN3Y6qcJvcjUyAiEyU2NTzL+IdmeOXwKbei/qu/1pudCrpyPR3Ih1UBsUtor/dav6/5stUpPFy/cAOMoSj4/FkqCnoa9BAmJeW18NaGea/ai+KAScO5dp5BAeVFu9WL7WBC0A8Y2n/R68fIJihXGi4wzERWFsOaKkinjmzK2h+yBHYXdVZkWQmNVQ2r4k9Q0/Rqud4BINQ7nyjX+tswKdNHoeOP4prZme1hnMXKTiM5bzxlKsCkXHMHJIznqnxKLX0DKCgzHzqGIYZFonXhVcKiUtpOlbUoxWmhSEoZEIiQ0jSCNGWq5jGpjsSWuqhH938O6YzPRkehlCqaHp+tQK6sppG+IPgWJYYt0Qp9sLxx6xEv9HMWrbDwgvGmEKMUKTFe63Maj7GSG09JA8aDKE1ynJ++xenJu8znT3P16es0ywNi2+PF0p094dF9YW9PmPmGphGUDucMKWXiZ9BAUvDzJueh9z1BDGbRYjBI22B1y2abUM3phaI96/MVrQJqSDHRNp527gjaE7cJbVusJNgGjDd0fU8iE+H8fEG33mIaD41D+kA6M/ijPTRY4pML/PaCH76mHD+EOZ72+iHP/83PYo2y/c6r3Pv+e/QIs2vQWOjUYS8SHCZsUFwP27WhtYJPETmwzJeJ1gRMtDgD2+U+qRf2CbTaYrot7YMV69UFsytH7G2eJZmE1UhKG8x8hqgBn70f0h5x2PekbcA0TSbUhTY30/KO1jjU9dgZLGOiM4l234EkPBZcwIrBWyVprtjok0UXoJsTuFhx6ucsrh6x0C1xuwIbkT2FgyUtQtdHItmCd87Tx4C1FvEeMTb7IJ1FrKcPCXrB2xlRI8mV/hMxgoJrG4g9qNI0uRdBVlGmWIYOj897UqrUMfl61mQZIIprmpwZYiR7p0TYrLa0yxnaB/rNhosTw/zpnAKJD4hd0l/M+Ve//3v8D2+/SW/26XWNxWe+lVRhrmPYWoqPoOigamuNrvSJchxkl0z+nhgmUyVQPAZVzqjG8TgDOx6AQaxmBTWmL+8qSqkGy0TeDNevhtRENKpMjp14Lsf7mYYLRms+P6cyqIliRhWxdkjhG8coY/r7IGCngGFyaG1vXI22emwZeG0lvVtevv4sRG4dywnr8OwmcpcqW5XBqE3VmCPrytLld5D49faHqSrzlRQtMltkYjgOqmPinRiK//1orx+vN8F0Muvdm4peLEN8elKtaUR8ts5WecDsPLOUSupHRUgyAQPEQWENrwGiFXAgykgmYRdZDg8PcskvMwEpml3KA3ImT/iwGQpWm6xFkGER7GhzyGPSlGkA1MtMFkC9ZnmCdYT5wY88hGGvqOIk/9QkQ0JDXtu5MlbNkFAYGbVTVFC9BBNPAaaGJUIplDLuyfqd+vhUJBOpijARcRhJkCJRcrGVFHMhmXvecHMV0X0HjcMZMFbxxmDE5jF6wXqXU3QEFE9O44SYFCMznBO268e89b07zOyMRbPH8mBOtIZkoTlw2AslNRajgdiDtYomsD7Xgs88UcGIwxuTPQIx5joCncO6HPKIxhF8xKhFY075aoyDmRCsJ22V1Aa8c8SUYG7puy4/pB68bdj2W1SUdtHQdwHtEqGds3/zBc5ee5e0PuW91y44e5it+eb5q3zpf/dLrKwSXv8BXThmfgXsJuW6ClbQbUB9Xr82RfoempBd1s7CbBlpWosnot7jW59rPawMB1aJmlgYIW4uWL9+zOGvPAfbaywl0h8/YcGc2AhiPVEUNTlWP7/yFOvTU6xkT4mdWRSH2IbUWtJpAtnQ7DVYt8WQ8AuXXeTSIqpYaWhMC8bmyo3WYvsAq1Ncu+CsF5rD6zTrc7q4whJhDiYKjUJMkT7l2u3W5QyAWJSCtQ2qga5bY9wC1xqCgkYPqx4rhnUMtHHFbOmJIdF1BjEG6xpcqVufUkRMJOFQGhBLImQZVNJ6jbFFX6TirYioX5BMwHeRfnMOfo7rQuZKNC1uvkB9JFxc4V/+m6/z337nuzxae4KNqNicAUEotpIrEirLOIPFlY2nUtq+FOCfZUUWRBVEZKu0ygtT4vrCYGXLaIwMSi3JIGulyMpB4WiVTxNLVWS0VLN+Hm2xKvsG4aGjfJvIk1G2J8BNdLKO34928nfJ9pCJe13GCrZqlMz7mMjywficXHTqJai8g8H7POokLR6gIUZT3ABDymG5v5odsFOJufLJJT+fpClXT9XMj8rDSEOYDS0BXSl6LgpCCU+mMQSdOesJay0pKcaUjAjNPBetHqWpaiPXNHAm9zNJMAKev+D1Y2QT1IlhmPxhHAO5Y4C2k/cYJ384V8IYk13dZdKHxTIACEare0dLDW+Mr2HxJT6Y9lB2Sn3Qw6KfWP1Dr4PiuhkWU425QOUcjIkKWkIe5fpD/eAJwUAnoELTUFxitzhQ3rxSK3xVF73muKIpIQkdsixkMqwqIBKiZmAk5xaY5LnYKZ2cV2zOPCiIf5qrvDMmGCoRVktADHbY8AzhBBUlJrjoI1sHzd5T7C1T7lKnFit9rm6HzWECm3P4jYk5/UqACEkMeJNL0pKwRtCLDY/u3ufBKuF9wqclL/7UpzGHh5iwIq0AZ7DWESWhRjG2hRSxNmUyTxSis6gatC8cimhImuvHi/R4mRHpSKHPXAhjsAtHvEiEZPBzT4gRwaAxMJTZnjWkPpcWlrYt5UkFbVrmT99g/eCceO8Ot77zkPX9noMjh5k3fOQ//yvoM0vC995H0xbTtvjZBUjCSwZ9fqbETcrY1eYCThoMKURca/FesTa74/38Cma+pL/bYWPKFfE00uwLmw5++Lvf49m/+QW2+0cs1HMuSjx/jLEe4zy2ccSUSXPJWXxMsAm5JbW34BrENJimpXvwGCdr3CLhvCXZnCrXtvMcT03grAfJcXnjHYjBRKE7vc/s2vO0s5bkEtru0agFiVgTiBohGHxIiLi8q0Ms2zMRU27dKrbBeyGJsNl0NLMW41o6hbjt8X6LblYkH5kv9miX1wdFb03l1DT5vFIsc5vbbSuWoLkKpBQPQkqSCcaadcgcQzAbNhewPjPMTERdj1/eoDeedGr4zvde55+/8S3eWiWwLYZYujMLUSR75sgM8tppTiUN4q7kGGQFKFX9x4koLIqp/j3wokbLtpZX33UoDF+YfHeiwCd7vL6nk2teJhwPx19+DQo2H1NDMxQ4NJxjoqizh7iOuQ666hszEicv8wam+mYYmBnPYYTMabs0xp3wgcnH1BoxNRyBjHMxzF8azpWxTBoiGAYZyx9IMcIEcmXW8szI0jvXlcjvJb08j4WYmApXoOqsQZ3qYGBmGZ4KVEy55XwZt8YPeTYf8voxwgTl4hOkpVXpyqXFUVEdjDGYHd2sg3Kt563EtYGAOJTanbwG677edNHM03oDQ1hARpS7Aw4m51IKXbecp8RjdfieshMS0OmjqqigvlMr/hWSY+mjIFRX0rgJK7tTqddLGFGMRjRp2So5vTEfmCd7vIOMMKcxr8pErZmeNWxQc6UzqzQvwLx4dHhul6tY5TPmZ6BkIDgC7t37NpJDC8l6zi82mIOWm9cOOXohENNZzusPmRat0UKyWKsYu81/G4eYkAtb2hZpchcvG1suHj9m87jHqUeWW6JCH3ri6Snx7IA4d5jWMytKzLslqpHYbXHOYI1kC1MiMQZSAJMcrWnp/ZockxNwhmaxIIQNssnrxvqWZHpEAr61qEaMccQuZLe6Udy8xYQt0bU4a3HbxOZsg3Nz+quHoJGL773KG7/3QzhP7N1osdKz+NxHuPql5+nvnGD7DTQe7xrYs2ijWOcIqSeEnAYYbSbBSpfXq6bMcndGaRtLspHlzWv4Wcujd+5yZV4slAY4SFztDU++/y73v/wdrv/SZ+jXkb1rz3Ex8/iLFey1xKR4zSmneIdPQgzrXBlQHKEU/rGu4fzNt3B2k1McvcM2MyIC4vPfxiKSm/0kVcR4rPeEFLH9lhQuQFvW3jM3c5wI603AtYGZt2wlkoJAMmjMmSjWWGbWEGJks97S94EYA9bN8H6JuBlBLUkMZjZDTzqca3B9l9tX+z6n/jUO43INgxAUZ00pGiMZGNuAMYqJuVdCTAWUO5t7FyTDdrshh7kC8yNH2m45OQ/MDw+R+BSPHt1ie7Hh9pNTVhHEzEmSs1hULJjs0asyTAeScdVNBRhQ6p5Ilk2V/DfEhkcpMoohEQazVWv2UlU9df8yGnYT2TaSv6scrSeesBl1avRMhzDKwUHuThT0CDoGkTW5h1Gr6gQYjIq+ACIz+eIHjM3697TI0gQMUDT1oKsm1vJgyNrJwKZe5vqzGF+qJSsucznq2KuMtpUfNZhvVQJnmZtkJH3LRMdMNZSqlttV0MhAVCyeHzH52abCs5PiRR96N0x03m5a45/9+tGzCYa5mzzNAXEVJDWuSMZFVZXOZEBF10v1bxV2pg6s0zT+mxAIp2zWIYTDiJaG0MD03rW+n+3s7BIrinNIGZQcM4oMlrSkGuqYxL12rOc6lroZp6BhAiRgMubx+zK8X66RRje+lI9SzTKQMq5ye7kOwGCjj/tKMoCo62BE8DnNZAijDeMuaUp1TMPeq0h43Iypgjab502K69GQvRZJlROJPDqPSBLmV55Cg0PXx3QrB8EhTU8MEbTJRD0RFJPzum1EjUMaQbdbHt6/S3+uNDMhxUhMBhcEk3re/oNvcXD1Ggcf38MYQ5TENvXopschSG/Qbc82RsRKdlMbj22FGALdNkCKeGtJ5M6K4AjrNf3xGe4I4jbQrwKtU2qOV9KcgobNYZkY+ky0S4F1EPqzLZaWcPUmmnpu/cZX+cG/+QEHGA5fSFgfkK7lxk99Dm9nbDYPsK4lNJ52tkRki9GIGMGEHp80KyOf+yq4vkdTLqcbQse8WWCsx6pj8fHnOb97l+7efQ6vmJwct1XUGPogHOxF3vu1P2D/E8/jru8RTgyzxVMkc4wJim2zozKmBNbhWo9Nnm2/xjdLxLTYg5b48CEXd9/D783xswZMfn7OOWzJuhCTY+1+Ngdx9CHh2hZnIJ1tCKePaBfXiEZ5/5W3ufaJG/iDJS4EEkJrDdF3GDsDLaGxFDPWNTDb87QxW1khRlLyaMptkluzZOs869u3OLx2BevX2G5DkA2aDIScfireMZt51LgiXKtLugrmgLUF0IvJHomY+yL4xtGHDpEZ2oBooHscoGt54/tfJ5knNP5FkoILM0grepdbKOdCV7EQagto19IIB7IxICNZUIuxNUiaiYEwdCdUxpj2VO4NZxkE2ERxV9CQioiWIq9qXRgmx18OYE8lz+TcU9m7wwWocnlqdAxCi0qIG3SFTI8ZTBNGhV6VtYzXqe/VYkQ1LWPAD8VAHQjsk+wBMeSQgUxuq+imHT1aZLpUzCXDWIdbkzxalcSUwJ2npIAJY5ASFq8ZY9R1V4GE5my2LPrHui8juCrPqhhj9fz1Z1UXAmMJ5b/g9WN6BuovOkFWUzwziYMMte1HItruseMk5O8V5T8U5IlQq/jtwMr8s6ZiTPDu7ikpLv+6saqyrpOvipTTowlCQSiDbh+V/OiRgBF162Ss47FSlfAOeChEnDoDOuQ5DABCihVuKgJP9fvZ3VMXSiX41SDVUDtBzGD1pxrrmhJhpmktpoQFKuCvYKreLwKmuvVK8aWChvMiTEgBAqnG9ETYiPL6+oJv3nnC39p+hLQXsDi8nhLWFouhliTGNNkJqhbFoN4hJgOi04fnCJZ2ryf1gqYG7w0NibOze6S44vbvfpvDl/5DdBGxRGapz+ONOcddZi1p0yGiGCKpD2iISG1PbTyScn8BjJKwWBPYnB+zWO6DBlIQOhWMdfm+k+KcAZOQJGiMnJ+tWZh59kIcLbGHVwiPNnz///1vuff1txAjXH1RkLll0Vq0OWT28efoQ4/DYpaOZPfQo4Tf2ByCMGCj0vd95mk4g3RbfKuElHDW06SA8x5FmDfXWTx7g8df+y770tFeyQDS7TlOX+3gocUeOKQ/4/3/7ms897/9qzTLOZvkccsGywkijhj7Qni1uVvkwmA6Q3IzxC4wbcPmB6/hGpDZIb5tMtD0DrUG187ACNY6gipqPd43SEwkI6TGYQXi+Rm6zfUhurnjG//od/nSf/Hz+MOncrc/7TCpJYklxlRK+pq8Xq0B4zGNzYWTiCR87gopwkUncJLgjdd58viAp3/hL9GtHmLDFm8bSAZjXAG3QjSARLCRqIo1DagD02KcKfvOYjWhsUNCh2lg0c7YktNmExbtz7h7/32ibJhda/D9jO16w1kKdCbvTxNtroNnICTNgJhRZWevegEkg3cAlJS9AlO2/QDYGXRo9q5OFK/IrjEzHEyRr3lfy3C+Kgeq/BqvBgzE7oHsvAMIyrETLtbYUncKAqTo4dG4GQ4ZFPdohOxU0JtY/x+oJFg9B1WRy+T4Id9ugjKkfKeCiqknYOelVUgy6rFyqKkyfMRJRSpngDm55qAOqrHLNJOunn/UOzk0lQoeUWqRIiOym3V3+ZUqoKiaY7dkz5/3+jGzCeqrECym+YZmMgHVVZFHNzk+T2ie2+yiNTYjHE1xmNER8Fby4Lg4xsVaUSwltlfU7c76K8p2UjtgBAOgpXYBKbtZVEcwkM8xQLcRFFQwMGH673bdqsdW9Fg9JgkhDSS8PKUFAVJc/ppj76PnLk02V2lrWYXENPwhFNIKGDE5lUqzC8mY/LeUcwyPsyzwqty1PJdhiss1KnkyVQCVW3OVY4WIkiRDP6uGx2vLl2+9xie+9RSf/6svsp4/prEGY8/QjcGYQv4KEU0Jkwyic/zeHGO2dCdn2KUy64WwEVK3ofGZa7AOF9htxF7f50E65u3f/BM+8rf/CunIoWzRbYeTiMRMM5rNHDEmUuhx1pM0u2pda9AU0JCQFIkaiWppW0OIyuoE2r3cyEZCztW3bkbqtgRCJshh8OLR5VOse0Wswx3ssXr9Dq//P36Lix/eRZPhxkcdci3S+hmSIu21p5hdWZKOnyD7EdMvaNMW2jmhEayG7E7uFTqXiZ6SwylWqiMzc0m8a3J45caLCJbzH7zDYmFR02N0ht9fcvDZjvRkTf/Y8+bDDdt/+wqP6fni//pX8E+1hJOAIjTaoTaHm6QHcYaQLMY2mZx5eEQ43xJPH3L0zHXO1xdY74gaMN5lpr9voLD9m6bF2AY1isSQRa5xyHyJ1Y7QXyAncPjSVV5/533+6L/+Ml/8B7/M1U88x/rcoT2lyE/C+my9pyLZst/DkiRnpPSdJYknhh5Hw+m3vsLDt76Nvd3Qb7d88m//VTaPn9BtV8xam7NYSldIL1KKGllCijjnCmlLgUAIqcR8BW8g2VzVUo1h3ihiWy7udrx/711UezwzJM252CqPzs84S5EonkZzHLf3AdGIlP4TsTCnQEuvhMz/GS3QAsYnKfKjqKmWYKmWX1lsA2Ewc4aMTr9WU+oKGEAZSrQPB1UDR4bvDNqufjR4HauZTJHJuwabAsbYkqZdPZFpcrl6fFXcjH/zYUBgVOgju2viEdiZpMk5L4MBsqdAxE6s+klqdr1m1R31uxUYiGRAVfTRWAeiPpNK6J7MT2Fe1uoEIhkYSij3M5HJ9RkYM5Z/S0PdgEtLYWKs1oi56vjGj1qD8McAAxEZSjNOEWGdcIFK8BAY4i+iZGbuuEjyMZntnQbrvxBqCnM3FzmaoLEBBFQAUOgXE1fZqMXK35oQjdTa0oMXoObYa069m76Xy31NNsuOi2UKlwsgKcp+dO/U7yREJVfZUy0AvYAECiFwQhJMOmQdD3GffMtlUUiJ24uiKbNLc4ZJ4TiY6uKMqJpBeNT4VEJLWDQjy7pwk+Q5mDo/qDGtuv4LgFFANAz7ITIWeUpiMSmgInz3JPCPvv51/g9HC27+5DWUJUYM2J7YJ1zo0TSHuCYaReYzZm1D3x3jrKFphM22J4WAszU9MGbq0ZV9wNKq5f2T21z81lf4zC/+NObZffy1I+RiBakjIcR+nb0/qsQYCzXEEfvsTYhqEGdwzRyrgo2eVYo4tujckrbC1gAh0geHYU4KHYbAWgFZILMZ7Z6h21zw4De/wXv/9KukhxeIwGwPrt5s0LbF9Q5xFv/MdaLzRAfeLUh02NkM2yfEtIiN2NATL3psu0ci0YUtbWxJCrZ08bMK4i2qezQvPcfFd9/G3DvGiwPb4p4+ZHa05MnFE+6tIvfONmytQIQf/varnL19n5/63/xPaD92A+3mrFcrhA50Q7uwrDeBpB60Z2sd5skx5myL90o4PGCxmNGv11gctp2h1mLb3IAqxJhz9WeOqAlj87pLztH3YOce7Tti3Id5w8Enr3P/ldf5/f/zPT77H/4Nnvn3/xLtYUMMirVmBKo1g8dlt71PwiYY+nWi226xAhdvvcvrX/sjFr5Fw5r3vvEVTlaBz/7Hfw0/36PbnmNToJk1YME381w/QcDJLFtfUYnGIDH3vFCFZJRITqtVIjKbY2YOSUuOj9+i8XsEm5sa9VvL4+PH3D895niTMo+3SFujrijfgIibmFNmUAbRRMZKrbbYNVURFLllSpv2ZAsGmBg9WrZ//b5GauVAQTIXgy0DoU+rTBAwNUOryNKpdTQxPiphDTKHZtCfNUtJGIBBBQKI5LEMpmq5zpTJX83sQQYXRV/1DDWcM84bkhuZDWrJCLU0+ZBpVm+HkvVWvwMM5fOTTs47uXedvlMA0jCn+dEYzFBnIKhmxS+QJHeZrT6ISlEbrlJq9ORU9kxwleHZGQbugYKtZPZqEA7l8XUU1jB4kXMvm5HN9he9fgwwYBm8GVMENkFsVcGUJcBQ8UnrA0kV8E3gy5geqKkeF8cY+XCjafh9fEzTlAnJ8fKqr2srqYpYq2egbirV3C0wZQStkxDBkJ/5oaGCeo56vrI80ujgg8og1SElp85XXpfZSyDlMaUSc5cCqFBFinu0egKGlyo1LQkEYyaFPIZFW9GnDjiqjuwSbi4htAma1Up2LMtIajGjyTYpGy5vnkIgRBHnCDERsXzt/CGHf/wV/u7yS7zw8ev45grCBmMjEk5IruPiRLHe4s0M5xOhs3lDqBAjmeluJJeHpkdnLU0E7UPOHGgMj09u8yf/+oJP/PRPcPXl6xjf4pdLNBhM0xI1YfsOmwIp9BAiqYukMMvPKyq6TcTyfGd+ye2vvsbBi57lJ2+wN3uOZEKx0COEGVEaaDt88GzPzzn7zvd58JVvsXn1HnK+RWZCjJYrz7Sko0OWQZCZpblygC4dBotp94i9Yj2QPCktcJKwHlLf0dOTjBA1sQwNneuGYlP5mXmsNtibz2Ks5/S1N7CnW05wXPn5F9mEyA++dpeTBx3bvse1ifm+ELVHOsvjuw/49n/963zkb3yJK7/4eWZXrqC6QeOcs9UaTRYNgWgsdIlbv/FNbnzhBvsvX4cnJ8yWC7q2IXQdYh240g3Qe9o2W85JwZjMI9AYspXYOCQqIQboEtrB/MY+/ocJ8We88k/+Ge/94Z/ywi99gSuffom969cwB3tEX9JSQySdbJBo2FpDf7rFhER6eI+zk2OSRJ7+xZ/jja/+IbE7R4zjzrf/mAfHZ/zMf/o32Xv6JqwvWG3WSG/pt1uss/jG5yJa3hGMkrpQiGCluVYCYwSWFl3sY+xV0kXPnXfu83B9jj34JC7AKr5Pt95y++4Fb14IT8gV4uLEq1i9amiuaFgttyr4a9hukKODLVkJhXmPZ1msCDF7WgdbZeJCL4ZJPT+Apn5U1lrTtsuPmhb+YUCgymNGRZ+jsaN8G94XBgAwviakw6kOGT4z2eCUQtCrAKBeW6agoI7pkqIbvlcmQyi6q+qi6gWpOmuwXnOK9VAVsJ5PJ/M4nQwGWU0O/pCo1QUo4dsxqyvPZgSRnCJY6mBIkW9JLxmeQ4GJicegXLdO7c5wKtetGIWpgjsmqZl/wevHqDNQ49BlOKZMbFkzVZFpRW/VRSHTghFx8vAq8a+u4EETMxAHq0Ie1mVdiAwLIf8uk6+n8XRaFmciL/pqWdSyw0MYPYzfra8BrBTAUP4W0mBZ5+qQWpPMcv5nysvCSMkc1TRUpdJyDan3WMtLGpM5C9SsgBE7TVF6VbwCY7vgAbyWfgJl4WRXdvnqUFOgPMmJkBj3kubnWZ7JsKkL2MlJCeNJdHBv5vM5IKVAsh6ToFPD98+O+db3fogxnutPwaKd4+YNKXqsJI72LH08wadE0uw0NX5NvwoIuRxtjBEjEesN4pTUZavTQG5hPLOEeMEPvvp1nr3zUZ7/y5/FXllACiSJaBIkWYwFlZ6gW2giSiLFiLQG6zMoEVVe+8e/x+Ovv8bNZxzHf3KN65/4HMuXnsVf2cPNTc5ZPz4mPbnD2f0Lzt98j+7OrdxcyW5hH3yCLjUsX7zKwraog8UzV9hcJB6+8oCjvx4wC4/ZurwG5nOcGGLocqMkmxvgJmsgJZwq8UQwxcllxZJSQ2j3mb1wk5M/fo3+jXfpWXLzV5/h7htn3Hv9AcZ2NE8JJhTB4HUgL3kndOaYd/7oKzy+9TY3f+onWDx/E3dlnyQB4wI9Slr3dG+9z+NX3uLw5asczvdo1olEh18uMd7RJ8VYj7eWqH3OS3cOY3zJjRawDo25giNWkU3CaCT1it8/wqjQSiIeRS7O3uTN33yP9t9cYXblCrNrV1hcu4Lf30NRNk+O6bo1i6sH6Lan0x5pejahpz24xvy5Az71q7/Ma7/3ZeTiEft7htW9H/CV/9cpn/rlv87Hf/HzuLAhrTpiykbBZttjrSLSg8mllUFyZsViQXQerCfGiETHkztbTh+8y8OTd5ntfZSOA1IQ+g2cr77PW2cPeX+bGxvlvRuLcqh72AyWZRbu2TsRKIZBxfZFFxkgFk9iNXry+SKm/i06cCGKSTLs2ZrWvOPxrEbEVAYMFh+D3JnK3okUqcKEsVug7nw0nHInLFBlDaPipv5eJOlOvLICgKrEy3dNLUYElWA5nuvSOIfvVm9BsawLCXM0ki6Bj6rcjZYp03FsE0CBpKEvAcYwVBUUIQ0F1/J5x1L1I6Axhpy6OhwzLR43NeFG/bdDRpfdCZfhfX6s149RjnhqhU/SKQa3TS0IUWM3FY2OC7KS+nYqI42mPKN1O2kCpGMKzviSCUCQCZbISnZwW+lEiSfNvIT6MJItQ8jpfKQJih6AAOPxBQiIKqY8LBn8PdUC19wnXqGmetQYkREtm7m6/6VgD6FWHDMjPMcYQ5iklFQ3YBYA9T8GITB6aCjKu85RcQ0KaErU9hWV/6kl7FJdsRVwKNkCzcAleznqI9lZBsM0lRSyOifJkIBN2HD2+AGhu6Cxe1w5+CjzvX1ck5AmwnZG2yzYxLuIGAwNsetobE5FM2UvN86hKdEZg6rFhIRTS3SWPiawW2699wb3b93lI59+mWf+8idwi3mu6RFLCmHqaVSR0JHCCqxi1ENnOb51lzf/yW/x6NVbPP+JQ3or7PUb1u+8xerebcS1uMajqePi/Ttw75i9q1fy89xz0CveRaz3xD5xdLDk4OgQ1y6Ro4bXv/km733vEY3b4yN3n7D85BWCQGs9qEW8wTdNcYEHpLFY75CUcL1wsT3DLpdI40jJYu0e8xdu0p1teOuf/gEHPvLM3/k0D77zHo/evYs5SKi3NJpooqETxcwkp811CkaxcwN2zersTd778m0Uz3J5hHcemXl6b3A2sX/taZ7+zHVO33vEc3/1J9FZJIiDGGiXS7oQWK02GDHM2r2s9oyQYi6YUsMGLs1RYwg2YtQQdYtGn9fD0qI+ESljnHes5R5x9Zjzk0h6Q4hqsHNBvMUQuXjP0B4dYPf2sMurNM7SHZ/Rzq+wf33Ox3/6F/je736ZmeswJsD2Ad/9F7/G+6+9xuf/5l/n5seeY0NfSKUKKRFTKNUNHQmDOEs0FlWLhuxJenL3Hm+//hZmvsLsf4RkrtLqARvdoOGCd89v89qm40kUFJdDlUyFdjViimFQDKaoJawnUytbkeq5HOTAaNdS92dxIRuyPBhixkW21LLtGYzoKB8HNVpIxVWmDvpH2fUL7orhQaddNlV3lNl43PiBDIDkw9vsKtmlb8pXzCi0yriGirV1INYMMnDMEGAAGlAymKZF1grYyHZ91V1Ty6ngg8ofq4BAycYTRQeWhPAMObJMrzZkKlkMVspzLEWfxOTwdBzOWcYhNT1w1EGqI2iZGmqT2STvCmXwhNfP/l0XHRondTpD5WcpzpBDWnrJki8KdrK4RuVfXzXrQC89jHqdKbKcokmzq6zrQp7W7FfNtf9hIAvmS5YSxySy18CMyrsguDyUsb62obj/yz3UWE4GIDpwEAZcroWAN8xFGvknasj1xoslP7iS8piTpkGhfxAlViFRlHfZKLXohCnPZbDvdeQISLUcalrRgIApCDeRSgWr2hb5Axkb5TkJUJmrFqEzIbuwTa6lGHql6zJZULanPDh+g7vv/oDZ/BqHe9cxbcONmwcw7yBajAucrZ9gzQySkjTgvMl8gxRIOHxpM52IRIlIjHgFEUtIPRt7yqvf/hrvfOcVbrz8UY4++izza4fossU4yZhWIa2E9dkF99+9zelb77P6zhvw5ISbn3kGM29o5w3uwKEBbACdB5wVrHHoMwe4q/uEbQdJ8OpYP3qAnQtpq6yC8MxHrtM8/wzr7jE/+Oevc3LrjJmNBLng+NW3WXz8KYybE9M53nuMWrBbEIsxEdtYtKQUhk45uH6dZCxJLV27RK5dJQXltf/bl9F7F1z9Dz5F18H99+8xfzplbEvEBM1VIi05JGEUs8yTYBqLsZlgmrqIs0ofHxA6w6LdozGCswtCt+Lgk1e584f3QCzazjHqcD4RU0fTtPjZgs1qRRcCzWyGNI6YIikq7WLBNkZULbHvca0vBVbyGpR9S7NY0HcnWB9zQdCZoRUlEpAmu9qtNRhrce2MkBKJxDoF9iLEVWJ2tCQ56M7u4w+e5YXPPs/J2ed4+L3v4ltH1DXOGo4fvMmX/+Etrr/8UT7xhZ/gqReexx8eEpctSTIptorPoGQ5t+64uP+Yh7fu8uTkEe2+x89fJqQbJKuojZjkOT0544ePE+9cNPQERFIWLwWsVwAPMYfAyk6OFHuk7C8pckAKx2fcy8V8GDKlKLKpOjBHb+FoIO0qrOlrkCYD2OADrx2OWJXPWsPAXJIOkxNM3evDjU8V7ZSDBoNLfse4mWQOMB2HFA5bAQCZmDIZtEzOATV1UCffVyZ2KnYsaFSezzDuqvzjIKHHcQxjL/F9Lc+qKOqhz0sJDYhYUnH7JoqDfZoBMOit8fQfCPDq7oxrCSekks6IGIwxA1hIaapr/+zXj8EZqIMxw0MaTMyKwupNSDkO2OEOAKP/Ow6umaEXwZS9z+R6JMbHME7MuLEqY7+SBBnBQAUG6NA8i1TjZJIBQiweg0RBVcWyl4LcUwUD5fqahoc+AJEasykPr+LNYRx1RrQulCngqceWSP9wYzLcbgaPU7ZrKi7fvKxkSCyV4mZMwzMb9lHFaUM8TQf3YSad5HHkPiNjrHIAwhWJDnusbogsyIzm1KmYFMuMddxysenp1om2FQyWbt2x6d5ndXqC90ueuf5FkskFfbabntQ3uMbTb7cYL5hGylAtNuafMWZmrbMeopL6QIgJ8YJLAT3wbDTx9jtvYN55G28dzWyO8bm6WOi3dNsLuoue2G1ZuB51PctPPMPey89jNlu2q8D526dcnG6Iqw0SBPUO1WwtfuHnPkE8fYILSuyg7wLSQ1orMj/g4PMv0oWet//HN1mfnDDbM2gPkjbc+9abPPe3voS0LWHbYVGSj1jJaWziIsbkdDyjhuQhuNyfQN2C9qkbmD7yzn/3+zz5kzfZ+/ic2Wee48HXX+fwWcfQKMYIHYoRm9M2vQHnSH1AkmKbBnEOhyHKCukS2if8rMG0Ddo0RONyxb0XnsX+/vs8fPV1nv7iZ4iPj4GAs4agPeKEvYM9+m1HnxLOO5xp0T5b+34+J4QI65iLJjmDVUu/DYiZ4ece6w19TKgRvHdI63Bq0JjBMqrgcp0H03jUgA3KNvUsLfRhQ9PO2QTP5mxFc+WIz/z0l/jKu7chdhidE1MuK902hpNbb/H1t99idnjEwc3nuPr8CxzdvIltG2LfY6OwXXesVivO12esNxf4pmex3+IXL6HuKs46oumQ1NKdPubuwy2vn0YepY6ERaJB7FQiFACkIFosyLyDix5MJfwrxZgw456jyCVKylkxJqqXwIziYmLlV/F9uW9J9SowyDAzBDwnsmn4vMj70np55+QfclqoMmyi2aZAQ/VDgMcY5x6U9Qe+ODlF/czYUWFX5T3lCVSDlRJiYby2TuVsPdZUT8uUaaXj56UQ1DCfMhqASAZ+WjhV1TgzBZANTIJqlImUGg8VG+VCW1W+Zm5I6bA50Ro7GQ8FSETN2QdRq9EqE1n9F79+9HLEA8LKkzGgKEPVsEPa2Yir6/embos0wLGhCmFFsJIt9MFNUo83imqNgNfFVUl/ReGmzKRnyA6g/CvoIDGUEy1QOIcOQvEMSGGuDiAqx/hsXY7D5kok7anZAsTqTqeUgNSKCJjuvgqBREd0OtoFFWboAIwzZilzShaGNdd04AWklBUIZnANilYSS3ZTWQRJJWtAUl2Kw9jGUsQ67M+6n5AMNGoLz/xeBi21BPoA+RQsOU0tt/Ps6PrIg23Pw7NTmtai4jCtw5q8Bmbe4RoIRKKu6VfntG1D7AKQaFsP5CwWlZjRNxYx0JoGSYmOhCaDEscGmkaR2CGz3PwosCLGCyR5UhTQnl4DtlGS5pz6w8+9zP5zz3H+6g94+PZbhK4ndh0pZhKfJjBbQXsBt8Tt/SRxdY5DIPZ5WzhhdaE8/Tc/g+4teetffpW4PWO+B2ELagUVz8lrtzl9/S6HX3oOYUs8B2s3qHUETTgjbEvak6jDWkjB0C8c7vAK0gjv/9ofcv9ffB3Tdyxf+gi+3SN6pb1ygIkxNyMyFksiJrJb3gu+balFTTLQEyBgto64CZAss8N98EDT4GSG8RazPODoE09z9598i6d+5ieRK4foo2Nc02DNkhC7XGZcHI6YSWoq+MU+ISRS6nBNQ580d5ycl34gvWG77nGtofcGu0n4+QKZeRSLcY5aQ1/EkJJimzkYk1NaG0E10Hcb2tkcVWgWlthF+pMVi6ef4dnPfIHb338V5xT6dbaaxLBctPSpJ4QVT279kOM7t/Dzec4a1oTzHusabDvHzFtSY5nNZpi9T6Ht9Tx/1hDSHBeUk/N7vHtyl/dWeWwkg9rRUhRNDLXtqazzLAlMvmTenaZag4oSh2dVw6tFIgzla5XiipYpCJgoMC3An/HPSqTWsqXruYd+A1Uml1r6U+9ulneFFFkBxVTujdK/fP4hwGHQJ+U7uY7vxKtRwhn1mMvkwZoBoBT5Xfn65dzVIK0e7Cphq7dg4IwJiGP0fk6IfFW/F0+FSqxWWf6eSYMBmUOx2TsbSy5n9mjWBNL8d87eKuHPIo1zO21FUuYXVEE2hGeKLknkrLIPzGY1ditIS6WU9oCPfjQ08OOFCerAysbUisAok6oTq7aSNAaFlwc0pB5evqNKdBHJsX1D6V8wtZQnr8rSr0qN4vquGQOJIa1ssPonREAADRFiGk5YGbqD9TyEChJSFIKSIEVc0dqZoDgBLvU79UlA2bRTxVs3Qn4vpfogYUgLUcYywGX9DikiE7eakJuZKDkPu5yFIQu3eD8MDCGuGuaQIlCmSLkMK5NhqPWyJx9W70BBnbWtrVDuo/SJV01sYuLO+QV3zjx7zZJ2YTA+4VJCJOF95m2YZNl2a6x6jJ2TuMCZ7NY0xqPJYtICFcU4g/MJ7Tb0mx4xiaa12GjQVOZXUs5tt2Uj0xBjn8vSOimkPE9MkYPrM55+6Sastxy/+gon77/DvFU20iEOUg+E/AxNVNRBWm+IPocSZBNIvTKfeTbScvSLH+Pmz3+O93/ry8jFfdyRJa0SzBIpwTwq/cUxP/z13+OLP/H3kdmSbR8wa8cCzYA1NYgGkjFIsoSo6OIAeziD3nLvN/+At//FHzFbJ2LTcOOvfJSODfNFJLVHxbKJ2Q7RiLGOtA2kxiPOZQ+TEQipZEBFghj6s1OaowO0acA73HyBEY8cHMJ8n9nz13n8+9/n3q//Li/8J3+N/mlHf3pO4wzeGqL2+GaG7zvEzui6xPnxKbO9Pfx8SRc6xM9x3rDVLZhcmjqmLSn2uZHQ0QF2sYQEThoSqXh0skJIRsA5rHUZkDoDfZfLF2vCuYbeKe3M0YfAtltx8+Mf5eHtWwg9yRfBaxxRwLdtqWKYsN7gPLTOkmL2WKpNJHqMNHjn6NIee/OXCCb3oEgmMOvh9O67vHL3Tb57/IRVtIjJLluHEERH62wwIMv5ddz7petCMZJGT13F4ExClEIV8nn3xSKHFIoXpci+euGkRcfIjgysgKDu98lGZxDUdew7aIKdKMD4lSINBmJ4yW0aeBB1SFUfXFJUE9e+fuDafMixjP8ufX8AM8M0Fd0wBUqUpnnTG5k8l51srulYh3L6o0zerSkwubTWFnRFJlf9U8u9S/YCYSQ3LSr3ldNAR9NxeptUXTIBLvmxfjCR8N95OeIhj74MLI9sitZyOlGuGTrR+PXGzUiy2wWLWlf6pQdVZ3Qs7FARb2bT6oQbUFw2Q48BzYZ+Gt+TJGNcDDIIKKmFebnqzkTWzQfF5a7FtZfywzXECVGHIS1y3LgM4xoqbVKt6+rWqsrW7AiFaSOQykvIlfQqWJk+7rzATNJJMkBxEamSqumuefFZqRTCutku7+g6dCW7I0d2asVUZWj5DLUedkkNNXVjGUNUy911xzvn51xfzLlxtIezG+w2QDIYaSAa4nZF3PRY8aj2hF4xThBpMc4RgxKCYPBYAyGs0OKKNqkHUWzKxMKkOVPAiikVDYXUCajFeimlk0GxPPXRGzz1/CGbB4/Z3HofjRfM9x3b1QUyy1UAhZzimVMdAQ9xG7lYR5bLGf3pY/rTC2TVE2f7fOQ/+hJnD++xeXwHO4+ELmIWghfFq2I7xSwtT956hbf/v3/Ax/7ev4eZrdhsE9utIaWOEBXjG9gKnVri4Yz24Ij17Qfc+51vsXnvB+w/7bg43uLsksNnnmW9OmV+eJW4BmkgWoWYWOQ0EOK6JziD8W0WgUaIISIoKQXmxuGe97h2jkkWXc5xzQyaJdrs0cwtq5mjuQLHr3wLefqQF//6X6YTiJtA6sEZj+oWsXN6l0scHy6XrFdb0jYirsG0OX20STO2aYU7UHh0HyuQ2hn7166j1mKSEPtsiYm1WGuxTUMwQrJgxWCwpUCQJxHp+h5XiLtmnjNzwqbHHcw4vPE0J0/uY1tHDAFrLJjsGHe1wiRKFzsa12TujEouP60BNOKMoU8XaH8HmT1H0B677jl5/D7fePcV/ujePd7aGIIBixLFYiUQJ4aBwOBRSzKC8Rq6q0o0yxEdrHAp4iyLg6rkdNjH032adBIGLSHUfMw4jtGK3tn1jEV26nsZZA3W9PQU9WZqkaPBoCvj0sRYm2b8zuh6F6S483f0waBTyt+XSuiNlf3G8+x8Z7BfLinBqpt23k6TJLLJfVSvx2DATa7PVEbX0G0O9lAfS5GrKnkNKZnAng0tECOkGjZIaQAEQxfC4h1IO9kgZZRpN8w86IOyNnbqAX1QvP+Zrx+vHPFgplZm5+T3KSrSOrH1OzoOdLwlxrusLP9CgDBVKZbMzUr6IxMAh0VdXDRDGWEt5L+k+QHHMpjy95BekxRCHDxfOsTXs+I3IkN15JoLTI3LF0xiUsruY0YrXC5hmZ01Vh+alusVwJQPn4ZL6q+XEPXlBVnQ9egWzGGLTFhJVJ+bFvJlJbcImUSXNKdB7m60ESzlugeTjJD65KTutDRYMDUWBgwlNBFDNMKFCo+6yEUQjNnDtmBlm9uVumytW9dhUKwxrNYB53NjGC0d5GLKxDQrS5wanBGid6UoTOE0lPhrjsdZVH2uPtjn8XprSQm6AFENNz/xDPtPzdk+OUdPVlijpKbB+BYzJxMWuw12IZg+YWv4xUBsE+snZ1z5yD4X372NPjynP1MOfvYzuMMl3Te+jfVgk2NmI10sGS5RCI3iAWMtd772O8yebrjxS5/HHzj64xVxCxIdoVdc0+CvHOIby6PvvMPp734NvTjH7xnsYWR7YFDT0hxeY9sH7PIQfHZLOpNojCGSY9BWQi6wZFuShvw0TcoktkKmbW1L6HuMmyPNDO88ZtaSjq5g5gfI3CNz5e7tnu/9N1/m+P4FP/H3fwWzsMT1mkhEwh4aOqwzRJvbY7fNgrRNEJRNUhItSTdEaXHNin59ipk3zPZa/GIPrM0u0V5JKWYvQFKkabCNRyTixYJkkmJcr5nNZnQpQIg4PyelRNvm0GI0iYNrVzg9fYixilifu1xq3rO1JkhKGUgqEeMsqRQgyyJOwSitWPr+Pmb2NBIuOH50l6+//qd87d2HfPekZ6UNyXTY0l42aKYjjnVvLknmicisAHvCjx904QAGqnyormuKDGRUWkO2++AZGOVvJSwX64SaEpSxhY52GVwi5JXzDaTHOvjpuYRRiBXDp/JXGOUdkqZ3uPv9KTiR4soc3LVZ/oy4QRmFroz3Oxm2FHk1yNhiHWUDplynZG1VL3a13qaOlXq7Vc9kIFPndAx9owx9fDQCIpmgK5kwarWED5hUl7U265aUirc9k9lre4U6BTrMKYNBbOpwh/sdeWXZA2Z25uPPe/3IYCDVUZjCYq2EQWCM6TCu4KIAd8xKqUqtoshceIdCoxk1sCvHlgINYYLWBoJgmvyeF/JQbjil8XfG72nK1rUkhdKVLLu3U6V1UrtImdI4SMuYBIYwQ3X1aHG/ZxAxCQeMj4bK+c9rXcoDKyi9utQ+ZL5rPKnOcd0CpnI0tChpYVxUZW7zN3Nog1TiVBU4Syl5XM9Y7leGDVmUulISRfNzijqOH6q3plgdZZOK2GGuNKWsHDCcrwPnmwTdjOWeIH6F9g2ins32nL1FxPuG0J8RY1da4aaC2zpcUUxiIqIxZzs4h/Em566bbA1p2mJtTmnTZCH5vPGc0ncbsC04y/Mv3WD/2Tn942PS+QV23tJtGxo/I862BO8wMeGaGTH0uWyxBrQQR9MVOH/8AD5xjXh6wXq7hcU+N/7yJ7l4eEK4OENmM6wzpLiiwWJ6S+pjbgEtEdsIKay49du/xcX7d7n5819Ej1po9+i6jrmA8Z4n37/N2b/9Hps3vkf79CGLZw85Pw+0e47mqOPs8RpNPaaZMT/YZ7sOqO2xCSTmTBgDYGe5iRC5ToEYQfpUOCaUGLzF+YDB4dsZ4hri4QHNcomdL5jPG7oGHhx3NE3klV/7Ax698Zif+V/+CsuPPp25PTEQWOJFsAAmz720OcbcbHqC6ZG1pzu94PSNY7YPHuGPZiyefgpnPDYmaBriLBFCIMWcloW1Q4EgoiLOI2rx3hBDwDctoVPamaEPCTM3JLPFmTnLo0OMcxiJJWNNcGKyB6Jwj0SEmHr6PtC2FlNCocZK4aOZ7G3oT9HNPY7vHPPNH3yTf3P/Ma+ewVlyNC6RosnkMadoCflUuZcky5XaVTaH6qq7mCJTanjSlL2YD85CPzKYVVr6v8i496uiGnKTBiJclUVplEEfgBhjtvAgG6Ze3qnSgbFI3NRsZWpJSyFKjbohf1QNyan9I5NU5ayFR2ih5T5LC+Jp1sHU+hqEpQ7yKVvHI9SiGkpknTHcZ1HAMDG0mHLd6jh0eFb54KqEyj2IjGigcApS0Y8V7KkZCYGm6JjseCnfrx72SSEpLSGO2jBumHbdnQHZmdM65h8NDfwYdQYKN8CYD4/zDL9W7DtJQq8IB0aXklb3VSHwaS7bmDkIcZjQfFghsAxdBssHtTaAloUZCwjRmIVFjSGnVPZQsY5rh8CKtmD8LkJOO1QMBcHWzTq40CKx8gR0Uvu7nmKwousPMxxLjUPVDVpjUx/mAagsvWFyszVvBvuiXNCYwWuYgGgSSO1JMG6nnUZNImX9140mhRtY76CMcQit7Cw5qv1RBlruRQZgriliTAYKxxvh/uYuF6dPcf2pK7j5Eo1r6JQ+BGIKqBhCDDRuD+8ciMO3lhjK808Q0wqTLMbtkUxAiUhqiqxRYvAIEDShahALUSOh2+Jnc0Jq2L+65KkXr7DdnLM9C/hmTtxcZJnnBZoWKzN0GzA2IE2fl16/zUWONLHfGs7PH7M+W9GxwqnFf/JZ5lf3OPnefcy8xYU5Ig3oHAkJ7Tps7JkhBE3EFHEhELXn5O1XCCdnLJ56BrNdsT7dcO/uKTw4ZnbxGDtLXPvM83CwZB1WzLF0XUDbGeoSnHf45Yy+WzAzCU0dKpG42eKMkFRIGnFNbusjJrOOnYMUc8tVEYE4Q5cGs46Y+RKdLWn3rmIWM7QxbC6Uu+9YZnuRrOq33HrtFe7+n97k5Z/+PB/9xS+w99GbuMWc0BqkaVA1tMslpBUaO4Ikto+3xIvI9t59LCuufe5jkHp82yLOoBpJxuGNQUIoTsNSVc3kbA6/8PQh4qzPayL60tMh5l4UyRO0ByfYFHHzBc1igek3REkkye2LjWSrLIWUDQQVatVBV+oN2MYNVrTxc9Iq8uDuD/naD9/kKw9OeOXMsZVcpTKkVCxfSCHu2kNlD9a4v8WMnBzNBpFoaYGLDkZDlpI6ALfatnawCKdks3KlnTbpRXYMo6h7dLDmR/0pgxVfDy9/71jcRX7vyCYZPppoSqrHeHB/kxglzGTMk69Uo0QvERKHIoIyvV4awMYAcKZhjiGGPwKQwYAZjtHhNrMCzgZvlngTz0MFIDKRqtP7HsYvk3nLHxqT91om9qUBfFjNhMw8bQJiBnBoKp+hQLk0eHmK7lIFNYOhOYRfBs/FjwYC6uvHTC2cPI1JaGBUZBNlJuNkDAsvVXdK9QAUIDDMYmWsyqh4NQOD2tSHVEMHMYOBbD7uegQUahGR+lNSicmkWFyDdax5MVh0AjwEU8ZnyoatdAZTQMyA5svPSsRhssBrhcJhjsqzibrjL/hACKDMwM7DHASKMhBTdngcUuakHFv1vgFcTWGqNSDymbLNWPxME7kwAJQKcrJgGhf28O4Uq1SOwfB9BmLOmsittXJ39YCbm5aj/ZaumeHchr4PECwprQCHWwiCy+68ZLAuN7FS6fBWMKkBFGc9qraAmYiIYMUS+oRIzEpDcm1719i8hmLD08/fpE8bthdbxHpM35HowYNpZyTxuBBJ2iEorQENHdo1pBjQmCvlnWvPZt1lEKPK9U+9ROq67NmwnjBf4GMkhYhfGILfkFLI1QX7gCUi25DXmm2Rg7xnNg/ucus7d3jn7ooX5nB4VTn4+DXiwRLVQNs2nJ5sef994f6dDY1Yto9W+BtPEc7nufFXsGCyNeqsJRlDtD04k9vyikIoFfFSBJtjmn6tpP0W3bOYdoHO9rAIbnaAivLuN98i7Xe0RglBEauIC2zjOW9842s8ePW7zK9d4eqnnuf6T36W6FsWXeT8/JjNKmFPT1i9dY8798740v/+lzm8cZXVY/Ap1/tPMS8o17aIZm/UfJF7B8SYiCFhxKLOYLxl1gghgiSPtRBTl3kzKSApZW7AfE6iQ6xnsdwnnAtKj5GAHdaIJTlDDIYUIykakkZEQ/YsKUBOK/U0nK8u+O1X3uVPji94o3NsiseAInZkJ7OqFLMplf9kkI8VUE+DbDLsGVP2WypAIKkOHoNK/M2tj8t+g4FAbKj6qHAFBpk9ypmqkqtWHCPPeQw5tF7DbsMJJ/u/WjrCRDoxbvxqPBRZXuRIBVUfrqdkFChV8RmooeSsMGtqe5ocX2RPFYyYUUhq0bbV4EEK8Ck9H4pVTgGAVfFnI3VX5mU9JqNhO3SVm1xrFKLjNGn9nhmfc9LiYU9DpdGx73AJO5P1rJL7OWSGSzFQTUk6LV7eESXVl06ew48GCn50MJAExDL0g679nyubXLiE9OpDhaF6E5PFVFBYRTr5DTso49E/wg4QKNoGUqTmZxY9VCa0tNgMuQGSqOa8zSSYIUxQXXO5sE9ugJLKXhk3sYpiSeRqlOOFcpOhyQOv20jr85goaM1LKytIU2ZA2amm9WGvCbqtvQG0zsVgndcj8tgGkVKATg6z5DdKZJBKGsy3Eodz5K1VGmYUrwjFK1CCCHnxqZashHyO4S6kjiP/Mbg8Cwnm/mnPW/NTrj08Z77/DO7IEsyCrXvCw5M11s9YLq+wsIakAe1yPa2UEkSPEYdKxDZuaOiBWGLqMdjiOYgkY3Or2rLmYoyIGojK4uAIv/BIOs/1yGxub2zVYvuWGA3eCWGT088ycc0QuzXJbXF9jwbBOGiN4JZzGpmxaRPzj3+UbnOMay1hlpWTVUX7DmskZzGYbO3YLqDdFmvmbJ1FRJHFHHs4o3srcHEW8dYx///T9ufPtixZfif0We4eEXvvM9zhzflyzqysQaq5pJa6USOssQaZaMBoA8z4gR8a4y/hRwzD+AHjB9owoDFTCwwko0W3pFK3pK4hS1mqIefMN+Wb353vPePeOyLcFz8sdw/f576seg8TkXnfOWfvGNw9fK31XfMLieHljvHkmOOTDRf3z3njjz/h8cNrpr2B2guvvPfdd/ml3/wa43qG3UUGbh1hZW/OhQ4VR+ckmyAVJOLmSFKHCx6fEvt5JPS3SMOAdCvcesCtj5E1XH/whKsPPqQ/EWQEGcyE2XfQS8KdOjxXzPMVD977kMvxE77wq7/J/UePuPcH3+Ptt69Z9ZHjHua98ODN+3zlt75m7p3zC/ZOGUIgbie8C7i+zyAhgTfWPcdM/77L2UZWljtGLM3W90S8FQ6a9oR5Tdor/hjC0LE62rDdX4N0FnuUa5iA0YkbAik55nFinoUp14wfQqL3tp9mGZnP9/zR4zPeicoe8DEheKJY+2mikEi5JposTQTRpYMosBQJK3VUimnaeEfSVMsEGT1ZQFopWyuu1hM1mi2ypMj5KqOqk7HhL4t7oEazaxWxWZZkfqG+TMAUh4I4GuFZBb/IAiDKd5UtSNX5DtSfAiBahFA0jeISyO7MJUMi37TGNTQafJUzDeAphfEKs8rjorqCF0ZWYpCawWT+WuacP3Np+btRLAWsymGREXney2Mkvw3JsR8cLog0M3JYsKFqfS/F2qH5rGJhTrWewY0bHSz2zz8+R2phyePU3Awhb98bZuzqTjhAS1mbb8wcy89lclbYoomELBp09f0belJViHGpG1AtAwoxB7bFWM93MbsGUl0e2tr9XsvKWkRoTsEFNd+eCHmRc4hJHlu7ETSb2ctLqm6yZn3aoj03j5RKDrIRVCpr2Aj5doNLZer2keXXuwONoCxxvbqc3/ztkextWb4QtcwL1+b5sjyuFlhCFk3HgUiiZkHniUarFMR5dPzo6RXD6iOO14Gv9a/hTzrUvczVxfucDmv62z2rccd23IOLSPJ4Ufwq4ujA9VZUI/akmJjj3sy8YmVGfWd1FTQpGhXU4TtPHz37vaO7dYLvhena4kZWvRB3E857cJ5h7Zl2I8MwIANoMKuQ6xyx94RtYvagJE5O7zC8cMqjuMW9+BL97bvsP3psaXxdxzCYBUPmIZcU7swdoqYpalIkOVa3T0gp0d0+ZaUr3rh3wTjt6BIcHTv09Babb73GRz94zDvffgdNM0esudLIhDIE5Y3vvMFX/u5fx5+cEKctnayBmW4TmEZreOT7zjSTmHDBMQdPYEa7Hh0j+4tr/GaDu32ERI/0a/zmhBRW+CQ8/OM/Bzlj6CJ7B8EZ2PS9N4GN4sUEYNcr4/kHPHzvhC/85u8wT4GP7/0+ulKeRBiC8ta3f8grv/NlVi++zNTdQsZnaNzhjzuYZqsnoAI+GL9xguvN/2qujd4KuzBaYyR1xIlc7GXCa0J3M643Aey6nvV6TewsPVGTY0oO0gxOcc5iAzRFnCSsTHlHROm8A9nj/YbgPWczPBkT0XU4nYneMk9cZvxLWnuJFcpCQrN1stAijbafmUciFY8mTd3Bqq8az13E+qL3Lb1KTN5qK4fyXRrBmjSnLksr5/NZC6870MK15WXlpnbPUgdlAQTLuMtpi8m+kX5lgD9Pea3PaWRHO7F6UqPF69woW824dQHDVXGp1pLFHVrqqhzIsHpuCfbO78JhDe9qldxiTZHGJZtFoxhILKUbRHPMmWZemgN+zYiR1zNhcXrZCi5FsRbbKzYwy067AV8o5myp6f9/8fE5uxZmfz7PB70dIL+D68jvsCCnBR2SmzgY4st3yW6BkimwaLBaN3GxAJDAJbHOc/kzlywuQKPFDSyphWRCtAGVoBwHuYofNUiDgxScTMC1ZaVb0HOZuLpqFajYVKmbo5x9mOpR0m4q58h7vEGa5WaVuLQBz8U+UHSKwizKCF3FSYqh0Grpo4DwCo0WkIMJ+8p48rclPXJ5chlXXtdqVYvNPshxBNqRcHy03RMePWEdhM6f8NqXbiGnR3DrBaZZSN0JWz+h0wTSM6wGxAvjrOzGRNcpKY2k5BEcXd+hUXBZUzTjkOZ2xzbGOc7oHFE8btXjxMMU8DLbHvKgSfG9WRe6OVjEuhdmollXtCfuIHbO8uInePGlrzF3nomZza1T4jia2bYLdP3G4jYchHmGaUaCJ8RIijOTc4gPVrAorEhzYnX7LlfvP2G8uCR4h2egf/WEo3/71/j4j97h47fe5ugO7FQYxy2rlaeLDtcnpqfPePyn7/KFv/MrjFcbdH9ttSdkpl+vqktrHEd8561YD5ZaF/czSRzD8W1CP7BTT+g3+NUJ2q8Jq574bMvlR+8gwwi9MHTOQPmcCCGZayJzNXEgwbFer0jP7nPx4c94+a/9Aq/d+4SffecN/GlimpWPPviYN//kfX7j3/s1vAe3vc1+PGMm4SdL54vTnF3TjuCDxTh4g5vjPiGhR4IjyR7nAgSHi94CPqMwjTNxnPExEXXG+551N5BcYk6zVcucs9XLCeLFYgSCpwsT2yniZCY6Rx820HnWqePhkyv2LpkPN3lCY2VMMS3WZUA1GUioICAHlLXqWsmikmwEdiUbgCJLLKMgCxVTPqj3FHVI89BCuUUgNMnBlQG45Wb5o4MH2NlVaB8y9SWwWT7t62VqB+6CTzna2IKq8bf3aWRGGV/9nAVctA+VRljXsTXX1boy7T3a50BOA1jmd7M4XlUCyz+pwrhajBt3h4DJp2KRLQocpb7DsgQg1k8pZVeFLEORYl5oXSmusT5T5Fv5WpC/zALdHJ+ra2H5afMpJrayrMJBZ6l6TYOuquq8zHBBmJZZYJ6DtACCSH1OFewxdw5MWPvt2gIy4XNrSAMD9hI0lWA9KhKUnIur5PhCsWIfpukuqUClhrHmAkdaOi8uMHo5mmkLLIDDSd1HhbiLbeXwyDtFW7M+y0XlDFkEdxXg0uywirQXBlT8dSVwUnLXQ+qSFDOUvae2CRMNIi3TLjaA8pZTatZWcjhH3sBBEpGR2Xs+utzz3fuPcN2P+DX9Oq9/4UuE2ydc75+xvdqzOjlhddwTpytS8iQ8EhLDelkHn0JG0lb33YvP1oDZepU4YZot8C/OjpRg2HT0Xc88mqAQHLOKRaDPW7q+Z95PrDaDEbB3SBrx4ojTDLLCExjniaOju6y+8mWuP/4Z3VXE06E+Wt563xOHnt5bnQNrt7y3oFSJKEKPNSaKvaOTAaIjvHCHD3/veyR2uE2HzI6X/vZf44PvvcvZx+/SnwZkmtlEz76LsDarzi4pJ+J49/d+zCv/zb+C3DomPU0EZlLaIU7xBNQJKxGSF2schGmwcVLC0BNVGfuAx+OGAYaAXwf8ZsXuw/uge1anAQfMQzDBN0ecKM5bMShVTLMeLODQe2X78ccML73CL//3/xs8enTO7vEDxI8MYeKNf/xHfOPXf531i3cQmVgNa8bxCvWKF7NwxCmRZmt45LwQmcElNkc9YwQJgWHdM8dEYgYH3vXoPCEK+2nLKp0gXhlTrk8gZgnoQiL1PXO0ZmXindFFSnR9hxsn9nJpcSlDwAvsz3d87+EzrpzHJ0u/tfj/XI8g05Bm4G7Ws1g1wFgUkSx3F7GzxNkU3iEUC0D+vboyAS3ugiKQi3BrZBZkt0SpZ/ApwlZKily5n974vlB54d9N1lhmRm0DplZOVGFcBPKBaGjue8NiuRyf9qEucruY+KvQzuMV+DRrhl2eZUm1UhehrxVvaAUAZfCpub6AAXumFo29GXLpM1BlntpnqVi18/Wa6yuIJFIBMXmdkuTQRcnvsDRVyTDP0gYzT44cjOF56PVzwNiN43OAgSLFGrQorkafH6YWusPnl4iUYtYvSK8K1PxSVKhthtEaIFiq/6GYGyCV9EC1jmMxa+5Y4xofrbKgRjO3x0TVoG07aw34MUGmNW2o5OeX4EYlh0s0piOpGwXQnHNQEajm/y+R9ZIrDJYpLJtpIeCKFHIGwQEdZRlfNhraBvTluSnVJ1WsCDZWQHLAYyq4US06m2jFecBMsrQ9IOw8gSZOgTrmWiQja0RKZmCNCdDm65jy905hnHvePU/M733M4+2WX7s442uvfZWjdc+4Pyd0t+HoNv3mDg4hRmO1xIkoMxqdFYzRSJwt8CxG0JjQ2TSzNJv2bq6TDrda0a+PrUa4Tzjv2cVIvxKrbugG0MCq96ibwVkjnV6EFEdEI10IxCSs3cCtV7+M2xxx8d6HXF0D/RpcQjTgXE+/WSPRApJ8l7m+JtuTe+s34IaVlWLegj85IYQ1lx9/xGoNT58lvvo/+G32IXJx/y1WR5E+KLu9BSuuxTFPkRllgzDP1zx88BPe/Sf/il/6n/y32I3CfH2B7xwumC95TBPeCQToOmvFq7Pi+xWaBK+JKXhCtyH6jv7kiLDqmJ9ecPXkE7p1x9St6adEWG/MP2nx8CgRPwTSDHF3RTesiKsNIQS8XrK9/wHDa7/DL//dv8n3/5N/gq5GOlH0+iG/+3/4e/wP/1f/S9KpQ0bHsFqTpi26t8ZT/XHA4ZimmTlGvLPqfjEmVusVESXOE103IATGbaJzPdFvmbsJ5gnihLpobqUQSSSCBtQFQtejKFFjdhJGoooVtBo63LRnOwspRtYy8IM3H/Hty9FqEBCJOfCsS5otAkLJAVfRxdKXadIVfgjVbWi8QjJfEqOpG/y7ND0rfKZokYXeC384QAOtjL5xv5IRVv6r5a8iDQ/892XMDb+qac1G6aUdcHPTLOd1QTjafnljfpUzUe+jzVrlBcsn52BBstLoarP2G8K+BSatPbc470twZ1lPtzyj8DmaKP1lVAc/l28KD2cBOCW+rAZvN2NykjPkDZwKkmMNFFx2gztArfS+3ceD5Fg4sdRtcWo/83q3r/uwHfJffHz+csQZry6mJXcICKuEajJWtdQmyLmc2iItKtoyP28CnbPNt6CvvMAlLqCkEZZiQrkEsWhE4gTTklpYkLHmhYGbwX8Z8JbOYGICzl5yNt81aNiE8yKIDw0Ey4tQPQwWqpvj4MWUwMKDmxysdCEm19zCfPzJ8qDz9Y68TmKbskTDVuGckXRlARVdLaAICu1lBJsjcA+vK5taq8WhguvCFJDqEnEOXLIqYynuIPRsBd6/djz45AkPLiZ+8dkZv/Hlr3D8wm3OLieONqd0PuDdAOGKcb42C5CaRSBOiTiNpKjoPFuQoRogsCEHuqHHOY/rgll9ZG0gIlwxzRPiIj7ANAqhWwPgQkKDR9UhMdcB14l+PZhHISb80V269V304pIHb73Nj59d8huhR6aI9yuczoiKgQA6XEpmMXBCjNbhDx+I3iPbiW7dEV54kcsPHyFnOwiOo29+gW/97W/ys//qn7NZCWMYkC4yDBMhCWlWhl44wjPlqHh3V3nvD/4lt1+6xYt//TeYY2SO5i6wnHqPMuFz6lIJwKNzTNs9IgHn1qT+mNBtmLuebobHf/xD5DTiBset1Qtcuz3d5Gz8WHZCwjT3II642kDXEzpPcJ4+DHBxiTzZ8vJXXufFX/kKT976EQxK7CA+eJvf/V//Pf7d/+h/xPDKC7h5QnYO3x8j4yXjuKXvHf1wxDTPSMyiy80kFev4SMe439J1PX7wjPuZGGd8UHTaw2S1DXzv2Awb5jRD7Mxkz4xZrxx4QX1i0hHxQjcH+vUJ835i3nW889Y5f++9T/ggWiS/OiHQMetMlNm6hVawbPSWKs9J+EyrPpv5YxVumZa08AOLsSq0K4V3NfnyhXK1Cs4mwE2LKnBDZDVCvrLiSvfFgrDwg4WbtQJ8AQSta+KAaX0aMzs48VMAQcUweuPKrKC1lx0omiXs8iZ4KdcV5dLkVusGscflIGo1F/jC6cx2WwurNYDmAKwUxlwVyCYzJP+sTe3KHCpOy2WkFRCHdxbsXsZXLDZFvmrmyyXTRLTE8FFFbXlfVb/Mlp/Pcnx2MFAepK7+3rbQrKmG9cgjzIttQRmepUhDAQr5ZZU4AGaLESgughxBLLEJ7IjJyg3nAEEKiEgzOk25GY3QhPGC5lK5WNRlOzFXULaUVD1zWZTykDUNURLgWRr3uBwxvGzYSnxi4Yi+6eZYBPsBkixMgwVTqWpudNKg1GwtqGb6Wq9BSZKW4ME6X+oGkmJe1AW0WGVOl5GjZpTvcqyTUMwMS3OkclsDEotOYhvaukOXspqSa5UUYirotIOkeBFm4Hob+Mn+gsfbLRv1fPHOEfFqy3zf8cJLLzGvRpAjOL5rGvo4oeMV4mYkBGCPzEKvgVIQyEkADTWgUtWyEuKcIKjV40fogu3ZEFaMcYfri8UBUnJIUKt62K1wWMZC6pR+8yWUwMe/9895/+MHXIjg4oj0A8ntcF3Ehw7fWXqkRqULHXhIc4eIJ2rEzxCGgTF09Lfu8tG3/yX9sXI5DvzCv/87bC+e4KaRcPsIvZ5hcOil9WxICHE3IhLxwVuFQe9xIfH2P/0XaPLc+e1vkSQyM4EqIfRE55A4I9Lh4gIoUUGDoP2Krj/CdYG+63nynR/xyY/f4lv/4W9z8dYxadyyHtamQPne+gSIELwneEdKkW5wpC7kfskThDWiynT2jO52z5d+9Rs8+NFP2RwL037EncL+gx/xh/9n+K3/2X/AC1+/i/N3SPOMDx3D6oiU9sxxtkJTQbL5dMBpJCbw3YrVcESKMzFt6TbCfOlx9KxXym4/4ZyVLXb9CZ2L4LW2pLWA2dn+CVj7CkE6Zb854kjX/OxPZv5vP3yPPxhndogVQVLMbUFpkLb48ov2YnrnoXbrFKIUJm7AIpFQZ5k3Xiv0rnxSMvGmQthF6xTNwcSF4+pzlvFG+hQJU10V5QunZl1dguAbIHDA1yt8yJH8WW9uQEnLs8ojDwRwa2VYlqXRnpXSnr74wpFl7De0z5pebFF5ebzPB3ZRAjkzCqRarAEoLh5Z7l/SMYXiB8087RBt1dTwIu8l881qpCgnCm2r6DqV3BFS8x4QBacW5IqmbDkwpVdL9UYBvNaquCXZj1TWo5ad+v8DGJAmd7PMWHLKiWTUVZ+cYecifSirVWmFIlxL1D9NCiEZCKhlAyRyrwHNsQKWKogqLkZLDUwRjTM6NUhSNefe6+LHKQukC5yqXopapg9qZTZdAv3quG8uTSuE81t2BcU3m2U575A51E1R12ZZrdr1EStVKZC7iGbHRw1cKReW/gqlEpgWcqJ2Acsb/YAkFdC4FCI5+EKq1uJy9ayWT9TI6Gr+NCGTNOdHy7KutiWKuTSxxfPx+cgbj5/wNy497s7E/Wf3uDzf84XXv8pwFNlPM93RMWwm3KpD9xPgGTqI00zcz3jv8EFIUa0E8WxpiY6EXln8SUwzQxSiE1xvecbeeYIGvAYgNzMSA5KrbkWpOR5cYOpfoXv5BXYPP+QP/+hPmD2s15HL/RkI9GuPXgRLy+s9UXesujXzNBHTSEodzjuCGiiaENztF9lePGX86H3ohdPXXuPuL7zMs3fepjtaW10FndGVp9NgDXXE4WTCSWQQMy2Kh22XGKeJd/7oD/jmELj9O7/E/vqMOe0YSLgozEkhgLImMpN0JukA/REyrJBO6IaOi7c+5M3/5B+zeuUOm1sbjl55javzj2GaSA5C6MwqF4JlY4gjiBBx+PXG9i4mhDXtwc3sLmZe+OZXOLp9G0nPkI2YO+Vo5Ore9/j2//Y+v/Uf/Y/58m/9EsJI2liDKjfPEGe8S+x2l3jFFAIEHwWNQkxK6Nf0/Yb9xRXHpwP78ZI0Bx5ezqynp6xCIM7WGtmHwMyEJEd3HHHdihidZRZFZQwzaRo4f6q88cMz/tMfvsu/2G3ZSa55UihLNQvSEteUFu2vSOVchEhloVOzJEsWO2JZPRWUNwCiSnZXMcCiLZZnLqrIooxkN2U6kFtF6lLdukVoK+Bk6blWWVSZgzR3aayZWS1VLcFwhdcU5fB5frlYFw8+rPKlZCMYb0kNaHFVeVoyz4RiDTkAPZRHS/NHIwAOhEF5I3mdaWK8ylq3Y9RPuU9Zq5K+aBI9T7couo5SnrlipVLhsSiK2RIbVRdgl2VTXVPJilc1fhzKsYLoln3y2dDA57MMOBDLIbPgB2ApPtT4bVr0WtDSskvzy4tmAdBYAwMtJdAmJ0mqq0Dm2fqCJ/L5eSFUc4+BOYOBHNVORn/kdD1VJHeIquakUmJYQXOlr+IckLrBCyrNRMqy2W1SqaK6MtHFVVU2Rt6gupQesec3SPm5X8r+LoiWalUoG781IULK5YOL2cisFgthV3iQZ2Dm/JTrKTiRXAVrCVhMpXlGcy3k0s1lXLWQSY1drkct9Vnmk3JPtgwMkggqiU4cqsJb59e8+c6H/LVXXmfqn/Lokzd59OFbfOHVX+DuF15n0gnp13S+s73mLJJfuoD4gIpjO++AZCjaR+Zpy9nDR/zk2z/jr/+t38CHDboTJJhpuKT29H1gvJ6wymBWbW5wAdUJ6YTIHXY7x50vfYVuDb//n36fpFs6B6lTto+vLU2NgRAGUk5PC/2KcZqscQw+p64lkAG6Du02hJXn3j/+E1a9cu46Xv0r38KtOmSCcHyEm5U5XcGmQ8UK7yjQBQsKTEItnnM8zYQgTDLzwe/9S8bdNa/8zd8GZq73F6yYYB5yUyvrSzDvHbPr6I+OYb1C+57rdz/hzf/4/4E+/oCrdSL6ge7Vl7mzEi6ePoTJ/OTJWbnwrl8xqyP0lv2hQ49GoWMARjRG25OzMPeeV37h63zykz9n2KwtCFFHs1p0n/DH//v/E/f+1n+Xb/z7v8HR3Q3eB1w/YDaOSNicoPOMixPzeA3RqriFlBjnyDgH/GZDTBN0Fgd0+1iYn10xHHvmGPADCME6LYowkQiyQnprIR1jIO1GHn0081/98U/4R0/u84NrYVTBS1ry/TE+JsnSlBcCzmZrNb4cczyBlp8tnRTo3rjztAiOGhe02BaSao3TMkVAq/Wh8L2UrytxSwt70YZBFWFO5udl/M135fsDCXvjelo+lgVaEaLPAYvn/jhYh8Lj2iZAedZUgVnWZXmogYeSzSKfUkug/FnN+fneGfRTeDxgHDqDDMfy+YFoK7xPIFuQ7awmrR4owef1I+8WAFGCCfMmSalYqB3qTPtPpReOM1koSD23gANXM0JsbeTgFRbe/W8aDNw0A33aYheTizYfSnNOCRIsMPlAvGp2D2i2DOTUwAgpJqu1nsSEeIq5PXFC00yK0QLIkvW2r0cq4r3BhRXVFQTHglTrOFMz15KkpgebcDF9WenbfGEtL9rq3YbS9ee+FCEzCqjCl+Wp+T+pahp1uEXaZ7CSKNW0MhDCMgYqSbXn1xvnDZfBWEGW1ZqhDaOR5pqyQhkQiOSYDyH38caAmCwzcXl/WHaoRcMmiWhwPN3u+afv/4Tbrw58/RuRMVyxv0j8+Ed/yul77/PCK19g8/KLHJ105icOHdKDCwE04nSGcYQ5srva8uzhAx7cu8fZk0fMu4405n3gE0QrBesEgnimaNX6hn4AdURydUtNqINxXDG88ir97Y6ffufHPPz+D3ErK440rxV/dc74eE93vLHMhPES5ztcEkhjBmU9rhNS9Hi/YXI9/XHgZ//sOzz412/y6lcnZjbc+tZXmXYR13n8vGZaCz2O2JmbzGfzY9qNoBb1jnOMQZDrER8geiF2kWdv/YgYJ1761b9Kf3KLWa+QeUJ3kegnoir7KeJureiON8g88eC//C4/+d1/wfH0Cd3rgbOzx4yP9qxfepE4TRyveraPHuFUmONMTAn1jtBtUBHWR2tGsYAmnSacDjhnFSKL5nnrq1/g4zd/Qj8MTH5mjoBPkHo2L87c+85/zvl7f8arv/XbfOHXfpn1ay/iVz1BlDkqEga0S9ANZWeS1ACCU5hjxE09jAmdtji9oj/dMAfBdxtiFpJOrI6FsmHSPRr3pOvA7uyae09G/uT9n/Lt/RM+2HquY+4rkRYfvmRiFE3VUpfJKTNyrczZdBI5pB1ZNMRU7qFYt8SqERaWvrg5i3XRLOk5zqDUgZE2CHjxpVeLX+YxC4sud89sr0bjl0P5tPS0AwWmFQVFOFc/fX7Mc1f+PF5YwiEV8NVK8PwD68Pyr+V3XUKd2vOL4F1Q1fK3QO3vXu97Y6gHmk3hm1l61e/L2NLz17dy8MY9y7tOWtsp5feQU3WTWKOsZM+wFuRWtttiCJqYLkksVuJFEn2W4/MFEC5vm7pgNZJTmvfUwhNheZdFNy6flxSMbEZJmgOvFUnRmgVFRywFg7KLwKWERDVGlOMGJCleoXQgNFCmxtBbIU4JfLP0sjI2d2PoRYi6RdWvYrAN+NNC6Qioy4AzG+2KOVEbwf6XmGza9sXLRfXLjJzLGJbo/cX1UuYdQawgzM3W01IpXxc3HRSO0QA2E9jmryzr1h4Gcmwc5R4u06MFPKkvDKisl4JYClcBJY7APuz50faKP3n3A77x+lcIHew1mPl7OuOjD86Q9zqUiLhANxyzOTnh6NYt85vHHVfnZ4zXO7ZXV8R5IqbI0AXkRNhtzYUk3vK+fejxLteHj4mj4zWQmGOEZGTh/Qk78QyvvsLRy3c4/+gTvvv3fxfx5wQiYxc4Wd/i5dfvsv3JO3S/81fojk6Yp2doUrbbCUJAQofvjphjwoWAuhXHtzc8/dEb/PR3/wi/2vPidIvTr36R/sVTpodnuNUK1YnQYQCiF7zvmLJWHugs+n2wpkMdwjr2XMW9WTX8jHZw9vB90p/uOXnliwyvvIAeBXR2hGjR+hwNbO6esv/oKW/9Z7/Pj3//z9heXXN6Z8UXv7linSaevfkxr/ytb3F9fkU/O5zC/vKKQELijIqnG3rUe/Ce4BRcTp/cjZCspkFyMz6uGO4es17fwvWR3gf664mdV7xEVBLhrjJOH/Phnz3jwZs/4Pjlr/Dy17/GyesvcOv1l3FDR3GbRU1M0bQ5kc6CSjWSxj1XZztc8AS3InQdSXo0OYJXopprQVOE6Qz2jquzmcfnjznfPuXeM8cbV9d8dAVP9wllJjnFpRIzJbXglhRpUGlLF+Usmb+3WAQLP7ArU3UNmGut0aoPzmuoroRiHQQTWn0CI193Q3guZH0gR+sv0vx6Q9Bmwl30hkb4SnPqjaPVBYvU0/bLGwoP1SpQOKxSQE9RRlq9k3qu/V6VSczKWF2vLAqJPaN9rDa/N2twoPTAQerhcxNt/yj3KAHyjfw7WP9GTlaQYBAgSWafKtUVDOR3ngFSLNYek2DmCrphTWnei1bfwV9+fM5sgvIiZXloMZfUwAulxhJouxgmOLSmEDY/U4I4WwxHidousQKarClQisicMwZSssDCOJuJJmoulENOodP8TjOxpOX5ZYiL18/mlcqLwgBDiS2pphxd8vKLP8v+aIiq0uBB9Ip9W5Ym5eDAg+8aC0mlR8kFV7LA10V70MKIDpCrBR+5vNFVLeWkDKN6GFKZY8wulKYyej5J52QFWFSzydLeoZMcAV3AVL6noLlycQN360a1YCnnqBqJFCJIiSgBxdMlD7Hj7WcPeTx+mdUxzJ9sOVofm1Vtns3cLgYmx+st0/UDHn+iqFgAGykRfDAQ15krKXQdk1fOL874UjxFvRCcmfAdSpojpW54jGoR32HF7GGSY/qTlxjueq73Z/zj/93fZ9w/RDbJmhMNx3zrV36Z82f32H7/T/mlr32B6dUOWZ3A9Uy/Bhk68GumBK7v6FaBMPQ8eOsDvvMf/78ZTnfsXeL+/Ut+69/5GmlwSCf0/cAcR9zKyu1qn90M+xHB3CxOBLxA8Di1eg79JDgPqt7cIl1AhsSDd98gPL7LenKsXr7FVjribs/+yVM+eu8RH/6rH/Bge43bKJwqV+PIT34488VXAo9//Aav/e1foXv5FtNDWL9kJX/TtKejAzqSc4ShM40FxfcBTRHvBnSamNOEC8o8TaxONhwdHbHvL+hTYN+f0l+PJBlRteqC3iU09GhIPHn4Mx5/9CYyKevTl1jdfYGXvvgyd166A33ArQLuqGdKEZ2E7cU1+90VTjr61THbrsPLlPf6FS4FRANx7kkXWy7OL3hy9pDt/mmew22enj3kk6cjD88iMSpd9CRJ1UQvRUtjkUspKytq5ebI8ei2HysLLTRirssEzBqtOl1a0nQXSjKCcTfoqkQCSebHhR2YoU7qY4qh9DDh7OZh7ZatCaLWZ1beciC9Gp7DolwZ/2giFxb0Xya+XFsCrpdKZfkRTbBgEeqtAOXG2A7Gd+NQZQlWL1LWZMJz7eBhASPlXxUUjWn/QMo2zz24XZYNWhw4ZSzNMrZLmp9rmESKiAenSCrZEGm5veQYrOwOL6mE4qyUfCwKcHnu5zg+MxiQEnXuDIFVRNWaZ9qF0UwCTf8BzVUDDQRESsSDzOByWpifDQigWGXBXFs+pQjMeFWr8hWVNOc4A/LC5OuWJhtLPI/ksRY3UY2jKQJf2oWzjIGYc1mdRbtVQi2bxYgvv768wRerVb5fLjiUUrGLZKCStYBMytU8lGpRJKXEHJR+665E75ZKZZk2EDKTUjTX6S9BcBY3sWRJUExKasTbphOhSwfGlBqiqAuZmVVGr5Z1keuj6zL+srFr/YNC8Cl3ZEuWcrXk8O4Yg+BjZEcgjGvi+hhZOzqF3WiLGoKABDOjhg7ve/qk1lRGhBijDdOBFyV0thmCwH63Z9rOuM0+b/sZwaNzrm8/gaTegt/cQJA1s3TIrcD5w4f81/+Xf4KmJwwnkMQR0y2+9Wu/SXBbdvsrZuf54I/+mNd/59dZvbwmDdD1a/ajME2B4XhNfzSgCE9+/DY/+L/+Q2TzjNl5hl443ybSnVfofMe+87hNR9A1bkh0M0yi+MGRpj1e1sSVEnzP7MzvLclM5V6UMARIMxI93cmGOXnScYBOOHt2xrPf+x4+HPHBj9/l6tklY5iRE1gdCSmCmwU62xOfPNlz9ac/48U/e5eXf+eX0b3CDk5fWvH4449Z9T1TjHifrIZD8DjpmZ3inUddhHREiBHFMQvsJo8cOXp/QkwTA7219I0OxGd2mKzxQRC6dQeppxPHPF/x7OkFz56+S9ev8H6N+p7+7m1813N9ueVotaI7WhP6meBnwnpg9hFNM/M0MU8XTOPIxfkF19fPmMfI5COr3uGHY5wccTY95sNnFzyeeuYUcRrNmpVcdhGUXgDFbWDCx9rR5tglFbODquJyepnx8ZxRgDegIEvBIqtN4O0ZAsUUYDSce6q4TF/ZHaflnpDvmxmbK3woKwDO7mH560W4N3QKB1bUha8X4d0It3TznBsWy+WEKpSWQH2lZmCU83y5F40M0XqLKm/KkS3AVp3WgQvU1sH1ViYTpLg9D9w05T43PilABMyiVR5ZdCIly5wWLJVBlzT7nPlWFOCiLDutwh+/WMFVFTw4dQfddXGu1qhBfLZW59grBFGfXVRmiZ00ZfS3pDXexHB/0fE5iw7dXMgsjZ5DIFVlbBZKMzBo/qUZopltQw6gSGqRlJIUlxIuKqpWOlZiLjccUwYJNBvHnluRJGZOldp1LNWTBcVKIUO9SUME2mQQlHmWLY5kf7cIz7VyRmnVfkVrPwSBw5dSiLQ1adEkhGhG2PV8WUIySvQoBUlqEwZQhP2h7aPcv4AiA0ZyEFdBg5q1agigzeaynP7sOpDi25QyxMW0We5f/i0rYv/UGfDRnGKFEB083c28/96OL33jmP5oot9OpB7Er3Kbb6sDkKIBNnUTpXCIAQ1jqM6JgSa1/F3pdjx9+oRVmjg5CjiNTKMgMhB1QHuh69doJzA55ugYTjvuv/UO3/svfo/t9QNWm559CsQp8vrXv8VLXzzl0TuPwK/RXnly9h7X//lTvvw3f5OjX38Z8R3xyrM6XuNlxf7jM977zr/ioz//c67iQwv6DNAdOyIrZDUAHRI6hpNT5nFGBqFXZdrCsFb2lw7U0YUB9R0ueMSDxzHLntCZC8ZpD31Pvz5iP86s3AkMx4SvDFx+8DOeffIWV6st8powAHOp2TFr9vwJqpF1gDFc88Y/+wGv/NYv4G7dQVWQ8YoXv/A6T57ds9TJaPDbdZ4k3oLzpomkih865llztcgOFEI3oAFcCqAJD8RxhJyFIg6Sd/guoJ1lX6g4fNfVNFoJ5qVMTnFH9vfZ/Yecnc1oEPphwLtA6DuIiWky19G4H0lppOsFug1hJXgJdH1AgsNFx9m45ckuQdzjJOAUXJpRzYKaBQg4LeA87+dMc0nM4uiSZjLIAlObVOIitCqVZmZeAHwRLlK0fbeoDrrwFsMHrtKeMZDl21L8yILtCk0KNXaq8IhM7wc+dRpemE8qVxWOWpWSoukqh8/E/q7xDiX+qUrYcp/mGlnAyoGEqRkGbUYBi/KyDHRZhyof0jLRej+tsry2fszvcnGZtu9JD+//3DPLrwUQKbUYQH03Za0yUChDLHgmWwVUFoBl5VWbMcqyjrldJyUrvBXXN8X2zzs+Mxh4/n5ZuJeAk5tmlGoVIC+o5biUAkEU835MuFlxUWGOuDmjuQROEx5rMqRzzIGCmqMpG9Mb5X3ZoqlqFtiuap+aYxUWf1aL7p4X6rUNb5kqGc2W9JD61CJcyzkFETbFd5r1k4OfhWgaIqiTUaLLAUWS55qAXBTJ5ls2mIEIu2eqz095Q2tGxiKLFiOqNaJ1oaE81gp+lpWtcKgSn2lHCayPPFi6ZltmtGyLkmJD9olmgCCNv6wUK3q2G/m9D77Pf/vka9x99ZRRH+K3QtKAuh6RDicO55deEarKPGfNKlmgpfPGNEWhlx4fhEcPHvJqf4q7e8r1uMN3PaIO7zd0mxX72bHVhOhIWMMbf/Bn/OiP/wTpZrqNY9pbwKLrj/jlv/YrXJ4/RNUxrNdWxKiHOUz89E++wwsfvMpqc0yaHIyRyyePefLwHspEHJ4i84jznm7doZsVLnmks/XshoGE4o+OwCmbU2E7jhAmNrfvEFyPxohKYNJIkmRFhSJ0IiSNBPGw2kAnBOdwc8/UrRn6Fe9d73DumjAYTZI80UcrmuMhxaxlijNwsBo5++T7vPkvfoVv/Ad/HZcS6cKhMvLC6nXO79+jP1kzjpO58lYeFej6Hrreii+OxojTPKPzjuggBIeT3jpL5iJG5u4CCVlrDp01K+qzvUAta6aUDffa4Vxg6BxhNbA6OmF3eW5ppmlE48h2jKTcJhms8uCM4jtlWFtFQ4/DDx1uCEyXwuNpz372CGYVCHg0zVU+qpR4oLS47IRKV8XNWIWKWyipKryuCEy7uPCwlPP9l9LFpWwtdtPshtXKZyyIMUnOEJICSApcaUW3ZEXoZjJiYUyyaP0tSy+m+sY3vZjbW/XTBOhSf2XhJPWntnw4C8MijbOgLzKz1N5fePXhcIuQroK2uq+LVu+qln5g2Wjne/NeCod+9gKKlusky4DlVovSc1hTr/zRyA6FpYYMFShobvSleVylYqy6JQzQpJBZY6XKDrMM5eXiwN2c9GC6f9HxOVILcwhMcmbiKM8iVUgjeQIFcRo6MSRdGgtVF0jKC1LLChuCdklhVqwiYMoBPpaSpbM2G7D4gJrqTg2qdM6haQECLhfmaP1Fy6JJ2cOHU87liI0xFg+gPUcLMbLozdaVqrl3hns/Bzc+90G73wrxNrF8VQuRfNPiv6yhAbr43LSUpwSzqpCZU0z1kakUI4IlM8Hl2aTs1HCVhVF2WCHdJUAyv49mPkUTygbfhWaxAisJ02Yteloh2qX7FPjRwz3feCfy8umKo6OXmf2E6Ca7WAJeLLpWVYlTMLcR5nYSZ7DIO09SpROrJuh7QeKOd9/+gItrxxdfv4McnRBOj5mlY7ffMY0j6XzH2fsf8taP3uLh049YnTrSnHBjB2uIu8RX/8ovszpd8eTeDvya/kiACc+K2e9ZxZFnj95nniNJhXkCTTv6ux3TDG4X6KMnIfQbC2xLcWI628IXB6Q3l5hGj8xmFu5Xjm7VEVYb0jyy3wXmMTEMQxYeZhWZxpGu7wk+4DZD3t4JlRX96ZF18bu1ATewVivGNGtkwBmAnhXLW1ej+Q5WnZCOhJ/9s/+C13/1y/RffQnxHXp9icjA6SuO64cPGULPqGpuvmBm2+Q8KoIfemJMpAmYEv3xicUFqOB0tuBfPzBPWxAhCoSuR0IgOes+qWKuOrKrCQfiHXiPdh43eIZNz7TLcc14ggSj0TQxp9HaHQePRIeThHilW3k6P6CuZ9Wfcm+ceHg9kvazxc7IDMlSKa2Rmlh3ObK4VduL6shNiVLNSpNKw0aMqbj4xNIeLdXUhFjJLiCbliuluUV2HAYvG71mR0Kms0LHjsX6mAlT8s/GGmDWBK20ueh0y3NqoZ6GWR0AgSxklywCrYVvKtlL5hzCEtNw0J+Ag/EtR/lbnldxK8ZwHPr5M2+sQj3zLmmY0w0LQY2XagT5EqtQ4s0aANTKkoqDtJlTfm7z+IMjpwuaP6lUC5IiSu1SZ26gVDrxiuB8MN4XlxL9kqVU5bZCHusyjM9yfA4w0KAjVajhdHlhC6Isb74GCmr9Z9kAmvsGzJRaA6IRlxI+5nM0mR84g4apNBKpz7Nx2HtuZluEeiZO6vetz8gCV1yeUnFnS7O5te6ZpVxkFazUtT4Q+LZEmoVnXiIt3cYXU9cNvFnBBTQCOT9kqc1wiL0X+FOaLWVGUEzzmr9rGIrAkmHRoEzKWhVpXfNn8+iqG0FMqGgx32kpEmYaZd0KJao5u1IyY6hFM4qJN4PIZnRmRnQdTxz84dP3mH/8Cv/2L36Zl18WA4diLsI0K5oCcVbwVrTKBxMQpjV6NJkbUqMRig8D4vZ0c+Djd9/h4Zsdp7fucnJyF+c9cdpxfvaIRx99zNXZM9LRzOaWQ2ZB1ZOC5nLDPa9988tc7yZEE8MKfDhmijNOhOAT8/VEdzzQqWXCpGlCJ0G9R30k6caCFV1vBXt68Lc8z969z0u/9EtoN+BcgvEK8YGYdoQexAdj/hLokuCD4r3HqzVjcr3QiRI6j+sGpAOkx43CHNa44xXT1TXBJ+Row3S9xzmx7OhutB0VZwM/ZFY+g/TCsAq4XvnhP/hH/NX/xf+c7vYtxHfIfgd9z1HXc/HgAd55XII5Ci6scGFlFT+nPRq3pAjj1TVhtcZh9RdUR6xzZIcfhXkabU+FzrISvKPrAjMR5zxKZ3UrnBWbIgx0/Sldv8GHJwxDAK84p6ATzjuLwo4BZuMtjoBzDh8i/WqDhA7nj+j9mgcXD3h6PRHV4pWsH8yIYC6OlK1iJZ4HDARUQ2iu++EzX1Ffia1q8mal0PrvUENfuMRBGmDruSysQchlxho7aeOes5T4Qp/5rWom3KzgVIG/cFiWlsTLPatZ++D4OQDhQDAu86fQ+4H78Mb9CoMt55Wh1bq7zfdlUeqtyhi1WbvyfTufw3TJJXCx/XAR9gtgYgFiB8+VvK7tZ+36NB8WS7AIpf3wYjoqfLcAKjFgl60mlX87AxAWd9BaCZYItGpx/rRl/pTjc9QZKCbiop3nFangzpBxrizTTCploT9Xk5potDoCacJpMcMpPlpEbkyzFW6JUt0GZTkreEQtUIZmr6g9u6bdVNO7DSxlILOASJtDwiwS4oRUADUJxEq9CoKKy7Xq1RofZc1UhVrStYKRGnCjZjrXAhyWQymRvjbOBVzaJhERC6ps51c2tJScZs3gJx2iVgowcAvzKKZ8yeZabF6p+D8r0s0Qx2ErLOUlW+Ci5LoCZT5OsJoPUDsVLvvaCNKyEMqXYvtBcvyA2NqaUJgAsy69uR15+t5HjOmSv7P5bW7f2uToqInootUN8FucP8tSv2eepkyQgTgnPIL3A/QKnfW7CHOPD09Io+PZ0zMefvImcd6DOqZ5RwLCKURnLYYnb8V5kgoydmzu3GJ9e83+crSmROuZ5CzQMSSIbmOpsDFZjOwciXFGht6CHlVxITClhEwTKqb9rsXz9JOPmK+3yC2Pdys6f0RMHvEj/aDMEbreEaPDBXBdxxRn2z9J8CGQeod3jtkHhqOO/dgjc0CGDd16xf7j+3gfmXM2hZ+tpO6km/y+ZoJPODHrwJz2+G6DCz1u00OKvPMPfpdf/A//Dun0FrJbwe4cToXTPnD18SM0JcJqIMoK9Stc6Jl2ivNXhA6eXj9ldRSyO9CjWKfINFtHQufNSqG9BVwmB65zOJQuBKPklFDn6HyPho6ud4QgeAfD0JPE3EhOPLMmxAV879D9Nlv8IpDoQqDrOhh6vF+xP0+8d/8Tnl1eEV2PT5GIJ+iMmx2ppNomM83XjIEMipOjFgYqdCVz4Z8L31EtsV7acLHyX7coM4X9VjrWgw8qJVY8vrgbTBBU7tEIUGGJiCsPrRyoKhCt5lsyrCqHqb806ETJYF9RWlcEVOWxjqk5FubGIszrhJsxt+e5evpNYVdLI2uZdg5ULm6LTwMhrZavN/5+TpoW5aUBBVUQ3kAU2n7fmh1uzFuXOQkRdVnBdmbZXCajh0GMVYlb5JQ1EYOCTg9hz88/Pl8L46JFuoRKQCQcIo8cQyAyWwthJZcPtutcjAwxEpPitbPUwLSnI+ZKghGdEy5JziBQJpO2NXXQcIZm82LZtDY+rQK+8XlXO7jgyYBFpQp5IRfzcIJIQFIEV1oi557zYAxEAZdz93HmerAvsVj9rIknyT4l24TOOUsbyqp0Ma9bd7N0QGDazLEeqnXTWdhAqmuuaDb15Rk3ZqzDlBT7LDYbso1pqAWLCntLE+BQl2sCpCb6P6PQAkg0h/CLl9xoMoMsKQGRBiY8lheegA4z/yZVXHTWsVp6yLED0XV8Isq3P7nm5Z/c52/+ypfYnEZG5/CiSIDQrQnJk/qZRKTTDtVcMGjuLfI7ZmQt0Pe3GONDQvBob3t3jDuQgKo1LvLqTHNMCXyPVw9OzV/sEndefZlwdEK8eogbQNOGIKDJKg16n1jFAZ1hTBH1E2hH6HpwgTSNeC+sb9/h+uwMJLKWHg0O1UuefvAud06/ZWb29YBeJ0J3AscQLy8t4tgFxmAuEU9vQHCOeBF2uxkXAt16wIUT/F7haMCvejoXOP/gfYIL9P2KSQfcOOY4p5TjPxSfMBO6i3TRIb6nOzomhQ4XYHf+EW/9P/8/fOXv/Hvoa0cM/QukcUcMJ6y/vOLioye464CcHqFzojvt0bRi+7QHTQz9GhcUEQNFvQZIYvTTwbiDlXRMPuC8lZEOXcjsxVIsXW7LLIMn+BV+WJGCNU7yg5Jmj3prRhTwZlFxkeBvMcULUupJs1gsQOfofU/nPW8/uubNBw852yvemUvATzPWOzOhcySPlCxyK9Gqs1gop0tVupSZvS+dgWYDWirgYjQayDRj1recfVT4WcHPIsY/spC29LPMJ6oWnvlbFf7VJMEiEvLP3IfDBA6NVVdzRpM0GQfNc4pGq0VJyAKpKA3JAL4p56YpCEvrc8Tn++T6NKVKXw2kawWmlbk+jBTP9yWPvX63BBMueKm01TYZ5ZwjHdynZY+yzLH5tJ5T5qymuOQcTrsuZRO2CDVLqVwvvt6qyFBbL788U5raO5J3VQkeSIrmzANR0BiJGQg48VZVIrtyE1Z1VlTMIurEqpPOkc9yfHYwkFJxOiMpVz8SbGFcEXvFayW5yoZlBUgy84YH0BlJER9nazc8J/pZswYVzSWXzPwhizutrmctkZtM0y/ap2GSVBH5wTut6CzHJOTNU7B4BRNZMFt1vEIs+ZmNkHW57e9NM1rrM6u7KKcHWlCjaeGpjnm5vtClyfNlXnLwCANFCzFCKQ99iGYbAi7XNoLfXDpF0y/RANmUX4k7C/6kWUDnHKYcjKiay6C6QvxK8ae2aNfuLdU3uQQ0FXS9EEEBOCkTjhP4WCf+2Yc/Q7rI73zzSwwvRAhCrzA7IcoaTyLkAlQpzsZwY44ZSQnVgPNXRJ3wfSB0wjyaFUpcwoeQAaIJvpgr2am4nPyleD+Qoufk9m18MPN+1w2gig+OacqBbR0GeqYEsyd2VrvfhVwQRyBNM048d17ZcH19QXSCRVAMPPnwMUevz3Bng3THuKMdsk+44Q66G00M9Z5uijiE2SnOd0QzSuN6mFeO1XrFPCYLQtysWK/WnL9/j/HsKcE7NDi8rCzeJGHZF06JMWHNpBRxCUkO6VeEzTHJefphDbOwGx/x1n/2j3n9b/xN+Por9MNAlwJxCNwdbvH0o2fEOTKsNsgE4xSZJmV/eYaTGQkdIPTOEdOM80I/rEgIR5sencWyjQSYzTEWQk8IBn586Jh1jx8Gun4gDJ4YjQ2H0BGdMEsGuarZJdDRyYqplC4eI+oiK7emc8fMu8D3P3yTt8+vjZOlwkRN9FtsQBEnqe7vwuBNyBfGnOlKigsh05rLgv7ATC+VvhdGUB69MMCqEsiN6PpqAWwYZWEqtUSuqwJ6cck3wu/nhZwfmAPK2Ar4KL+XcS8PX/ggHGRmJahoqMy98ID8ufHxBglV1U6X+xwO8saaUa0Dy5hpmjOVF1f4VGtdae/fLqQuH2m9bJl/jZnST7m+8De/gMfM5yULfr15fnluHZPY4jmX94kHTSar0BwjUQClA5cz4lRLAM1feny+mIEyCZdN/5CRHg2iyxsjOUgzprNqBgCREA0dhxhJufug5roBmgrQy4F5JZJUltvW5SoCKEFp+FFqdR9uyEbKlPGTQeXyUSa0Yjx3ODKq1IhkDbZ6/kWaa/J4Wml+QGSL4C5DOBwbNWCk3LMIbhO4y4ZNMfsZ8wYpBLdUWyxDaJ7X/J5yASErZWkm/yVquVmpBOJtLpqUGvGsGSRRQGvN38DA1eLuKFvG0ow0azEL0yuR4eVF1PLHkgMxnbMae+p4a7/n8r13ebI/5ze/8A2+9MXb9EeJkBmrR7MLUAlBcb2i0aEpktKMyJ648/RdR2KH63prqjcrPmTBDdbZD/CuQ0SIKQftiMtVDoN1I5QOxBF8DzLjAiQ8zgXSbC4wcdbR0mlEOo8TzzTOdN6TQodG6zi47jtbQRVEJ7peePzDn/Dqv/vbjH7Fan0LH86Zp2v6zTHz9SVdl02HBj/MZ+yy62G1wgVBxQSnOzoi9h6P8vSnbxLWjtl30HcEF1DvLeBvjrjgmHNpb2suNaNJ6DbH+NURM9CtN8zTTKCDec/H3/sBw+OnvPj6S9z66h2QNdJN3P7qS+y2E3GbmKeJ66ePuX72DB8mQueYUrLujo5cH0QgeLxfmWtJPUw7G0vIxVRcMKtA6PBhIMY93XqFX60IQ2B6NuEF61zpwAcP3kA43gIJfYCOgW6OVtfdjyCKpA1v/+gBf/bhezy4SpjqkhqCzZpZ4TV5g9eiL0rmDY1wlfwja5xafdhaawNIFoqOUvwrA4Sq9RY20kih8n3RqA/M3rJ8L0Kbe7+EfbecqLmurStSPmt+P4hfKPfQZa4HApaMF2gFXeY9pcNOFfhpkSONwrDMBZbgrjL2GzESN43hhe0Ky1ppHlQNHixApgy+AJZ23e1mN9MAD9avBTtl2espQrvkWgVP68b9NCBWBN4iu7TWMbC5WIB87r/jFJJZnWyHZivJ4Sv5C4/PVYFQs6Bexq7UVJADAFcmYK2AnUYkJvpkPQY0UYMDNTnmJFSULHmziJGIlClJnVpeqiyapfmkEaj2SRFcjWDOLyIW8NIijOzLM7O+VgvaYh5zh/hUD/fRQQBgC0ooxFyGIc1ZWqNCaa6oPCULuVZYtgzqcL6Hwr89ls+VUvrZbDmVy2XYZkF4mmYQoXRjKwEqOkfLUy6jV1kYVVm/dn668KO6RTSnI3p/oHgUq49HkCikDlIYiCJ8PCb+Xx895c8f/2t+/ZO7/OpL3+LLX3yB9UYRFyvNOARNVjUuqSMlQaPgc843rOi6iB8i2zkiPljL2KTghaClZajD+Wxmcw68Q6MniSKhx3WObjWgEUJn8/dOmFJCQzZLB4fGmUGsm6I6Z/3KowE75xxhvcJ7E+rj9ZbTV27xo2+/xdMU+PW/+2+hc4KjE/w+oWnNHK7xItCtSMkAS+g6YkwEHCpKNxzh/RFz1yOrFUMPT3/wBuPunHDc42ZP0oTDkcQxx5lOAScW7DgnvHeIHxAd8N3K0jBF6NeDrUXqkX4k9kq8vuRn37nHi2/2nLz+ZdZ3b0EPYRaudyPz2Rn7syf0fkKdogRWdIh3aA99DBU4htWApoR3AemE8WqPF8xK4APeJ8KqI7lAJyu69RGyPrFyw/OWvu9wycoVuwzIUprNDSPmCvTBE4aZ5COXANs177/3hN/96b/mJ493xOSthHDrZcul0l0ODiy01vJZq3lmFOnyei71OhbCXeRrSQNcgIQ031WJIgvkXpgAh0DgABAU4SQsPYkrRS7EWH4pAk5pFImGjxywlAO4v/y4IXAMOMQ8xtJjoCh3koPiykWyyJXCo+ujsmArQvC5seefN+IAtAURda2o48mm5MqLF+FN5YeLJC2u2BvHTaCU5VTJM1nmsQCF1oqyeNg/pf18va4AyXqDZb2zy1u01JEQROOSirgk+n2m4/NbBiAjJwsuu7nHbE8LSCSo4lIkzjN9TARVNCoWD2GCQ1TMx46zKmR51bQIWjFCVHQR/HkVNYMBLZ/Va8umKS6AKvbKKctx8LuZAS2YTZHa9MMSGEN+4U0YQp5DFkIs+6cAyRqok9emFXz5kY2gLnYNNT1BtFZQLNc8J+zb99Iykxt+khJUaYAuVtdIea5rzHpKzPNc/HnFtWGXuLz+ahwzM6XsRrP9kRmd8Re1z5wgWjyuru6BZaPbfyef8ASim1EcXYLO9czJ88Yu8cHHj3nz2bf5G49f4GuvvsbprYHee4a+t4yqKKQYmGfYjXsuzj/gZLPitZdv4ShNcwzcedcTXbJmhSIQY3UNqFe6zuGcJzlrfayYdcQNDgbPvBN8sIj+0g9+WK2YZ3vvaTRtKM3JrCpJSRFErXNidA7vPfurK9brW8yrnv165A/+j3+fu90xX/x3fo3dAEO3IqxPEZmYL3cka81IFxwSHHjr1eHDgAxHqDvC5dS+7Ucfc3HvE/q7K5j3iOtwRFyygLcxjRbg6aDzHp0V5wXxWOlv5/F9wDuHOsGvBtIkCMeI6+EEOvXsrnbs3vkpcq+3Yj+uNwuTbulW++xqtNoDXTTfdBLLFFBNFgwpQhiGzL8cKh4nBrQSztw6nbMx+SNc15O6gSkmpuka6QIhBJM1eY9rGo0+XTB/uFcYEn4dkEcD333zjN9767v8/v1zzvbWmTAlt9BryYiqZNYKysJrFoBclBbJz0+tAKigwPS3gjeWWmWl/XfxQee9WARl1UDL78Jhc6EbsQHSIPGWAZUPKx+4oWXUkUke66crGcuz9FDQufK5VF5dBWTRpMscqkDP37UN725aPdphlnsUQSjt9Jr1KUy6XSdxy6MXbXABDy67vFP7fbNmVVDfWIrnsgk4kE0HfLn8WZXU8kEzn1Tmt3x+Q5CY50WLTMlBm7nMgnzKEH/e8dnLEadIbYsriuRoUYuqX96CCUYgCr06KyqkEBSYrXZ3SAk3R6t6piaoHan6zKT2fNa8x5bAmprbXtp8ApRYAS0bdyn9Wfphl1oAVNM2zYIuv9ZgP5dJVZf3ni/HkOJyUcXhmssNk29ezG7FrEPRohcAIM1/y3WiWUe3Cee9l10HKCWVVMuCk+dZN8Thofl+KW+icl9jtrntZQOgYsrdNluA0DC9tj2xNLvNNe/I5fUpmpLRbNGE5KAmw8IIln8RqxUhKjhnXieRbGZPPT/bOU4vFO/PeGH2rHogTUzjNWkaSbOyHyf240yK15yuX+XF2y8wdIGuH9ExF/iI9pzQ5TKzEjL4S/RDIHQ+azSRwXdM+x0pznS9Z+wDPvVWBTBNOLVIeOc6ophmOviEzolZzaCfckxUl5/ThY4YJ9abNcPxEUE65vMtw+mO7/yjf4Bn4pV/66+y73q6foWTE4gQ9okUBd+bgEsOIh2r47to1yPiidcT08cPefbu+4TBI8mTUocfejS3FnbR08/CJN7eZUgUBGrxZ4p4T+h7i+rvPY5A6oGxR9YrwnpD2u7QTWAVknWT9BHvri1i3gc63ZDiSJo9kYgbwOFBOqx1tCBdMCbms2XGBdJo6aRRI5KKm0AJvaUYknnDuN3h2eN6jwRLgzQbkwfpTcA7BSLEjpFj3HzET99/l3/+5p/x/U8uOJutXHYiZQUl01/meagcUGorl2qKYTGZY3vfaNRV+WGVMbOFsdCLSLaGltinUtVeDw2xVYC2v1usSNG8D3iJikmF5YPKL+zFFCHZCJhUJVRzK1n4ETd4TDWzZ77mCi0XMNNE2bcacv27pqDl8XDj3uR3fIgB2g/axnCL0L/x7CJMD0DUjbUqzxe1dS2F6ZYiD80Y9ca9l/Tx6pa4cUjdS9x4dnOr+rfUIaqDqimL2Pyy2cqCu21uLs8j43oDxDXV+y8/PjMYcDqj6iyaOa+lT4mEA43mq1MDBorgYhGCEz7mjIJkjMblVAmJ0XzySs6VXBa2edMZQdlmdWIBayXIzOoPiBVnwJ5RCk74LByTFrReQthc1chrpcDsH7b3kOrLKnHDVrNorsznwBLBIvS0IsvlDZQaB5IBxtILoYCXIlDtM2MAyXhyDlZcNmQ9CyclaNNZCmMFBs1GrICitRqUjxVPk/VhaRWZHrI/h0z8ZfOWzVX/lfKoxhQ0Z1JoBkyuiThO+Rwh9xPwgQyRKAlN4hKiHlzES4eT2u6DQIfPaYmjOt67HjkNidAdk06VKT1huj4jxj0xJnRyVuVSjrkeR662l6y6zrZ98Dg/0amQ1OM7i4/oXLDUQB/o+i4HPSckdRAHkl4zjyPSDYSjHTEJ1hTKQXT4AI6AT2ZV8a6ztEYS6i3FUoIzeiE3l0Lo1yfo6gjthKut5fzfelV54w9/l2dvv8tf/Z/+d4jHA5p60677GSTk/etQIuvTNQwKU0In4eyTe+wefEBIE7LuQHucT6AzgQH1iRQTznm8xyxRccbFjqgWqCmKZV9IT79SXLcmqtJLz6wT3cmabnXENlwSJEInwIwPDvWCcx4hEJwneo/4CY+lWw7rDeIsBTMmtVLKPmTlALTr6TfKfA3MHXPyqFO8WxNdhwwrnB+Yx4m4v2AYAt73Jlg762LonAc/49WyD9LkEK/sHvf8+Kfv8c/f+FN+eO+M87lH3GwpjXgLNG5aeRc6qnEyRa7kwGrFGHPKud/FH6ySYwWwbBpJZhKOZIDrSll0i/x3khuZZZozTpiyzPWFGWfBUbT+BT4Ud2b5PFPVgfWxfLcEKxVByQIWDqCOWq0INd5px9KHUS3adrlfZVR+4RstaKi8L59Xx9vas6Xy/oOj/l3u3Vh8CxCp/JRlTmW+B4Ckcae0cqcOL/Pppk5Effk1XqPAtrIcSk1hXFCh/Wij4cszChY6AB0VCi6YwTlqU53GYqIl0E4Nx0l2C0U1ECGQY83+8uOzg4FkjMsr+II+FGKcMC9gft8JUu5ImFBC7jLocudBYrKAnuJLS5iASDltrjGjmQy5sXj5hZVNWaPRnUVAJyfml60bXOp7LWC3+Gpu+vGLgMx1RPMXy3XUl2ObsPQSEykE6+u2okGBkitLaY1DaAhUC4jQBbiCgYo88Opb0pxjWk3+yk2HkAUJQs0VrubNFqlmRKHWQrWAMM0ZBkWjWYoYLdaUloxtDPneUuIHSnpPfrcSQRwlfoPKKGWZV76nEzGA4zLgSBbI5nMErYhasGD+3/WsfHA9sZFz3CxsNgNzd2Rd/1JuwCQw6UTorjl7suPO6SnaXyLT2sClGwmux3nNgXd7htAj4lAX8RliG8gOSJqZtiPruytkHtEw4jTgXU8awUnMtexnK8AjnnFMhM5ZgN6c6QmMHhDCsLHKYscnBOe4fHrO+pUTbp/e5eriGW+/+Uec/W/e5Rf/e3+HW7/wJfwEU7hEjkb20ZFioD++RaRnvIDry2eMzy5I2zO6jWPeAxo5WR8T3Zrt1Zl1BJWAuoQEJaq5GtBg6b3qIXeu9KFDncMPDhc6XDJhKccb+tunuOjRSZHVGrfp6RkRjYi3uAsLanYMYYVqn2M6JqZpZr3u6XsrIKUhkPxEcB062bvxqxVxhLjfoiiuA+k8fliTekeahOl6iwJudYfodohb4Qggjtkrndxi9jMaJ+bOkx7AH//pT/gHP/1zvv/gKePUZSAwIWLpj9oA/oO9L6Vh2cJfaslczYGsIiSxmgTqwCdzFSS3aI7JWfaS5L9bmYjkTJ/sV1+i8RdLqP3uF1quROlog/O0fi92fjE5F7+ENtdpqSWgzTOkUPpzSoxd3gCBtihQXblW6OexlN8rMGkYQeV7+bk3h3mYXmU8WJe/69HEJFgFzsVlcTA+Kc9sfy/f21otKdXN/A+CDyvTbsbeAgtlGWTz3OLi8Hldsiw8PK/IIVmAQ7VilPEt8gtcdnWXcFSpQd9/2fGZwYCfJSMNpWuDzygBY/nRKae/IeZvTNH80bkngTKTmC3QIaccWjMnyYWxSrpFnm/ezOJuLFK+tprPtWj81ttpaeqTDq+TLPBZTH4HZSVvmNlbw6A2/5EqMmW5hxT9W+tLLci8ElpeO4dUHH2Ak3XZeFUgqxRFwd6FLOBh2WPFv78MdlFqyi9LsFLp+51y/bK6jrVLljtYXwNChZiXtUwpVeuKWT3IZbjNQiDC0hURn2mtWeMCgEQyG3PZumSg04vg1axG6mYcglePOmHSxP29Ekzn4uW4xx05ayE8R2teMytdEoK35jNn5zN3Ttfg9iRRgvT0671ZvZxDug5NAecCLswkmRECJLV+B3vYX1yzeek2bnUK+z1pZ9HnruvxONKsDKvetMyEuREwJ14nJQcb5ujxEnDdQJLA6viUx/c/ZHd9zZd/8Uto5xjZ4V9Z83h3znf+4d/npTsvsjlew+0165dvc3x0i7OPnrLyK/aX13z09s84+drrfOE3v8FqpaTZeiekOJtA7zs2XWJ/taX3A2m+Yh4dQQfUR3AQnXUHdd7BPJHUEVaOsN4YgEgDaYb+qCMcCRf3Lnn21ie89ju/THIONwxoHPGSLQEdxNx51HsLRAwa6WNk2o8gid1euL645IXXTolpwrmVuYi6gf54RjUQtwIhkboOCQMpJeb9ljTOuN7aTqNHVvzHW4Mq7zom5+i0I10P3PvgCX/0oz/jn7/xFj94tOVaA4FkKYkEs9ww2zwLL8+usfLeUqEHqE1/VKxVt5kQTTAW96YTOy+5bGkUQZ1lLNHIk0KnmulBizCvghYOze83NGnAgvV8k5rXxh5Qr5HCQ4rw0sIpFlqtg1FQictpi5jJz2w1bAFXMqJY7k9zCgZM5OB+hRlkAFQ/b8KjD7DFze8O0NQy9vqm8vcCh6WIbwCE1mVS+Wdhvqk5v2qUdbyNV3UZh2TZV86tJ5WxNrLnRt+CRkKxMPSEOANzBVeVcataoLJzzmBiMsXKHUzw5x+fvVFRoha8MYXG6nMnNAe8lJWTvNbOTGg624hzmhcZCEjKYMBCcG3zNKBiQacVF9R1Fs0abNZEK07XjOOqeY1qopMskGuiQ/msLna5tnliuV85p9nYIk3t/8okyr0ysUiTSkQeYN5YpRqi5ueINi8/ZVKTYhXQLLiLW8PARmoWRfIG1bZrYmP+K8JfDsZysHMXACtYY5ds3ZAWQMmNTZg0R5dbxK24BtRpk45ZntEwDcn7BYNxlb2ImmPdE4zlqXFMly0hZka1ANZRA/fGRNTINim3SWyk56g/ovMeGawldnKOHYkHZw9Y6YtomJhipO97urABr0zxmr5f0YUjxAXU74gSSOoJk2NWwfWBy7NnbPan9KtA6AfSuMUzkOYd3g+IJrMUpMg0Jbx4iualOXJ4ipHQ9fSrY/ZR6DdHyJHjk/fvEfzE3dMX2HfnhM0xcUyEY8+4nvlke49wDfFhwL/pcHFm3u0JKmjaW338Z54o38QdDQzbzgRIMrO97wJdf5ckZzDv6fsVoRP2WxDnzYKR36eZxUfUefrNgHQ9gtVb0K7DHwWkO+XZ+2/z/tt/yumrp5x+9RtE7+lWRzl+aIe6Ce8taDGlhPeeab8HB0cnx6So4Hasb50QfIdoh8pICD0Jj3Qd69NIdInkHTIcW0GrMfH0wTW7q4nNSUe3XuO6FSGsGOYEyTOrcL295tnDC955723+xbvf43sfXvJgF+29ksArc6Y/r5BymptjUR5ErH6EJUFloVXNvkVQGfM33mD/VA0IlEq1hf4NYEitVqhCLVRWCtUcBt2VQN3lHgvhawXURiKtkCtuOlmIW6GWKG5dBEWJaaPOKl9vggTrPQojuClsXP7OHX5f+btZNA40+htCVPPnS7pmuU/52y/3b9b+4DhYI5a1uwmiKpNr51NAUZlDASrlOHzWjScfPrcIeWlPbK9omLjAYh3IsgbJLgaWcdZ7ZSurw3i/M+XL5z2iJRbvMxyfGQykmKvyJWGsPgvFazbtRjNNpJwznDIRqeZUHE1WYlgjLlkuc4qNNBVY6tmW5bFNUbt/lbEAB4U2JOvZqqC5cBH27EID5T5SCanJhKjPtLHb17YJLJiwDYIpvxlxFIF2ICQL/VRkefizPbG81zLXRpTXQEEbVgYsWUAvvRYKyxDaQE6bf5mq1MHcLFlc75nvgWquXmXfpRQPNpNq7iqXlmBISxtuCKVOxPzpLSG1RhigsSpIpZ0g3iLHMx60BjUOr0sEsKSIF0cQSCnweArM1zN3p4m7o+PV9czdQRnWG6awt4h7HFdx5u0n99nIhiC32KwhMtF1jqPbPV1YgfbEpMwaiCJWPVCgSwHne6b9zMWTLS+8fmpa8OXOKioy4/A4l4izz/jf3AOpIDfnGaeRMKw5Or7N9XbCrwb8ek2UPR/++TscrVbI3RV6vQe/putm0MSJ79kzQzQ6Qhxee/RYQEf8aOO+evaQ+WqLu32HsBY07ojba1ahR50j9CuOwgnX5x8CiguJIZhFQ4q26skADCtydHRCoifFgAZhNRwTjwfGR2fc/8GPWB0LP/vuH/LN1TG3vvgqsziGox6HNVZBwPtIECHFmc16RZwnNCX64PCxY573oNAFR6IzRcMrSo+4jv5kZgw7EhNpO7I/87z95o9J6YrQ30bpOL+YuIg7trMnpZHr7ZYH1+fce7rlk6sr7u8T0+zpfCQmISTHjBIlEmTOJcd7e1W5vkiNxWl4R3GL4cTCBlQIaI1lwgWiMz7oslnPVX610LSKsRoDC+X+DS+psisTsyt8L6OLm4Jf/AIiWkprhPjyMxOvFsIstsoiCBsGJkItXnPDlF+/lxufl0C+YoavJ7uFR1Qw1aR6N8chRljiogwwZTcJyzuyF1fmWsaRBUD7zNa8fxAnUR66xF0sFzZWZm3n015fxkPzmVue69p81eaZ7Yc5BuMgcbWubYnZEkgtaFwehQpRwYK7XS4F/5cfnz21cEo4L6CJOUsZn9MCDZXkALxoLWS9WAR2TBYjYOZdcyNocjlq1QZZgvSkfQGSDdhirtUF+JmETID4lMEH1W+npRdAShlbONCUyxm7KoKrSa6CsiJliv8vWx0KcGjBmxhIyI1e7V4CtaUwi4AvR906usAQyeCk3UoFQJVXXDII6lS0aP+FozRgo+7LZaMeVCFMDQS6iVRSs8mUHFEu9R6ShX1jeKgWGCBnCki1zqjkks9Jc/BLYVzUnP4W/EjDLFyO4q38xZk+5jLy9SJIMueAy0VMYlKe7IWrGLiaPHE7EzeRW1Nic9TTeUcUB6Fj6rbMXDGNngfnHS/eusXdu8f4o2S9KRKM+4mUTFNGFNaKn4V5FMLg2T6+YnfU43trnTufTzi1ve1cZymL0UoPi8wUZDPPkW59xPr0FrspEtZr3HqDP9rwk3/+PZ49+JCXv/gqabNi0C3MJ1xvLy1Y1wn4iBtndEq4zqM+EFLAaQduxqPE5Lj/5tv80pf/NtpHOnX4zQoudnT9iuQsxa8fXsPpM5JegJgFw3L0vZWNjonUrVndcfj1MRoDguB8R/IdQYT3/+T7bK8fMxx1zDO8/d0/4Gvz73D367/Afh5Zr1f4sMJ1MWeYzIhOJCJhs0FRUhxxMbDWFfM0E8cZ56zSoHO5CFK3ZooXjAn6fcf1o2u+/8Pvs533bPoTrreBt+99zI/uvc9PH15yNSvReWIQpkkIHeaKIRBWwpSss6U6h6SZkKyQVCoNhJy3lECX93NF1tkK5sh0mnlWrsnfWgtqgZkMhB0GoJNYV0aRXB/DlTTcRckScQs9ZiFgArDwKbcIYFc4hJV1LmV4OaSwhbYbUGPKUclSapUzbYR+EeA3JHULNBZCXtakApQqpZbrXJNZVefdDLf9vfrppfnZBANS1jifdxCI3jy27W54o1CQMUdZzr0Ri1UDJusUpFmTXCimCHe9AaZwdopflKKDSeqN+ZdSxBWkFYAhWCnkRhhlBfaA77vC9zUX8vs3DAZEwc8WcJbyB5pgUpAolB7eJcWtxlim0mqxbDgMDKAsWfwsqJliQi8LtbxbzZuylOl1CnNG42ZFyYvkyALU5zQ4M7nXvuPtRpEiRBfheCCg6+ZZ3lcV95KfWUzoWVjTjLNq32V2hUAqGCkosIxlAQJlRdpf8itextKGRJQX1W6MVP+T7/3czcqyN09rgEIDCOwMyeO0vHZXGpOoEZPmpFenTVRFdc8sTgJ7ryXrIIMdtd4AZm2y831+15KvtvcuNa+2RNMaf+zYITwaYZpXnM/wwi7y0nbm9ibQr9a4HnxYQec4udsRpy1n4zXxqXI7HsP6mhBWuL63NLAkqM6kGeYxEkdw0bHf7bn/5kO+8JUXGVY9vpuJsSeIBwm4sEakI6YtKp4kM1ETfr1ic+sWIwLq8OsNOqx4/O49/vS//H3WtwZ0s0Lw+H5gWjmGkNDtiLiAJEecr5HBgXf0q445JmBlcTjzjIvC+f17PHrjXV77rW8gsiKg4HviNhJCj4ZAnI6Jl1vcerIgX4AguNQhccfsBmIYWd++SwxriBZ7EWVABO7/8E0+eucd1reOERfxnQmmD978Pk8vnvHV3/w10vqYKJOB2P6ILhyhOhN1ZvaO5GZCXKHzSJyFblC6aSbutswxEZNjJtCthGm/Rndr7n94xtvvvcFluma9OiXtAh8+vMdPn3zI2892nMXA5APiBC+efmW0GcQxkkjiCFlZm7AmRhpD9ufnSnjJ5RRArW6CqvV7aWih+P0d0TVgG2fu0wLls+BWWVp6F+VBC93WtLgiWPIgnQXKVWGIuZvs3NbHLtW6YHJfFvqGBsnncdfmZZkfuZjlWkbE5eIbbGHhnZ8Ss6BioOVAo27G3uAHG14j2Aq/qUyvfBeW+1RLSPmpLFUWCxBoxlQtOWW8bWCka8ZT1rUKm8NriwmnTisrltXKsMRsSVEQ6ygM/BjouQmouDHmclGzxmpyqMa/aXlm7sOiLosUrTFnRU7g9N88GDCTfilBbMFlMSNLzdW5TDBajfjSesHiBiW3lLV2H8XCXcxZXswPV3F2TUGxhc0Shdp3gPKsUnmJBWyUVB61jSOl1KnkCgBaiG8R1AvALODDnl/cZ4twLh7wAg6awJE6skKah1p5OQo0WFKWtLE0LOCoHoVgMuao7oGC3AvBA0hpcemyv95uULWEFiS019VF0EN0cYM4SylWJ6W1sy73KltQMfcminjbmEkX1C2VYJf0QzA/l9UUyGMvPeGzNcfl6GpXx2V7QlBEA6ou15J3RBEuVNnv4WJOXIxw+1o4HhKna2Xdg99ENn3PcHTKbnfOs+tLruZn3Bo29D10nafvB9LcMY3KbpfYX09cXZyxH6/Zbq+Io6NLv8krv/IC3XCNu+4QNxCc4yoq3oGkADESdcf6dMPq+A5TcLgEJ7Lm2jv2j8749v/9H9Kvk81RhL7vkHCEjOes+jXqPXGO+NQzJyWqIn1AvKd3+d1GtRa9U4Qu8N4PvstwdMSdX/giw7BC+wHnr0nTzLpbw2lkdLeZL2E9JKZ5i/gNQiLt98wOhlsbpDvF6doKCPXGBs7fvc/73/shqyMH3ZFV7Ex7S/0LK9LFJW/8/h/yxV/8VV78xpfh5IS4D0ya6NYd0OGks/cVRjREBNPMZVJkfU2ftmyfjWjYc7Xr+eStezx4cp+ryzNcD8NwStwNfPDoPj96/Anvn+84izNutSLkFsVl/4oLuaFLAPVEAdSCmxVvRZxUUfVZXkhm+Mu+FUnMebu7rIwo4LNpPhaAmq0DpYNhqScfM/it/npngl8KOq48sQSq5foLxSKQhVvhX4vQleWeUv9zANwb7SYHOS68YzEzF1ovAlaXsZhEOmRMRUjeLCJUQUhr6V2ubddo4T2HrCkzYg76GLjD+9h5NyLl5cYv0v6e73XgMslj1FQtwgvDa8YivmXxi+KLb6xE1k59iX2zZ6tYfMgylnITjIceBMeXL4qLJUsMAXxa4rpqUHUz3hQzACwgzpjxp8CPTz0+ewBhjAi5zCbGjFRK+1vNef6mqbUpbweacYklcIfBewYccqmg+rIbXzLF/y0mrEpgmphgdhW9ucWrI1L3E85y9iFmn43UIiHma2+Wq+4DXZ5fCaoIWllepGRz13OyNRO+lqccHpLfalJtaCFVy0oFKw2SrJsAWVIzDwRxIfr8M//dpp3Q3vMg37fcr33Y4aiLi0QpmoMsqYsZAGhmqiUIEm/iPCq5mp3U7JPFPbSwLxPvlp5nFiALgHEiz/OMCoxkyc7I804ysxfHOAlX+8AD59m4yKmfOekDm5XjS1PiS68n/KaDbWKbZsbrc/T8nHm3I20jae6I88ScJqZpR4wT4ix2oO9WfP9HP+Ho9Dc5Pj1ivL7k9LYjDQH2iWmKBO9IBI7vvIo/GRi91TEgJXZz4slb9/nX/+SfsksXmRqtWmEIA7OHI1YkjahzTPvJMJUHiRHnreyxd35pmytKihPae1x/xHt/9j3crJz80pdx3UDoj5ivn1rq46pn1d9m7gK77Rn9UYebO5zuuRrWeDewPr1D8oNpvjiYlcdvv8+9t99iffuY/VbxKeGHnjkOkLOEot/R9QMfvPXnPPzkZ7z2xV/m9LW7uI1n2q6QISDdHhcGCxiM1mRq5hwhIPGI+Rymcc3Te+/x5Mk7vH/+FvgBvxGIK8ZL4cMnH/HG+T3evrri2U4IYY2EwRQF7y0/PvhKA16FJJ5JYCYiEiGK1U9JFi+j2RRrFOGzz3+hkSIzSikV0lI2XbKwLZ48EQxoUPFwFY71vovpq+J141uZPde8c3kOBCwCZqEirf8pH7mFByiHLrv23Kr9NMKwXJuBd9Up6jwaoHJAnS3jWE6vGRLVn99e0w66qlSHIKN9Ns0a3LxFPS81lywK40FYs5S1dc2SmVCtFs0m2Gnh677+vVgZGndFObKsrGtV8UCq8QGHA2+CI/P19fOyD/PY2kZ1FYSUrrYuM4vPeHyOcsSZ2UYjCKmauAWulZQ5e4mGPJfwOJtUKkV3oE7+4BXnudj6L1p38kJ5fdbeOO/pLNecmvknlbxc1QwxqG4vsxBlBN2uvy7Pac36pQog9cXnZcjnSeleJjcA5MGGtQ+lPnCJji1CtCDMRdA2DyrLJEVULptm0fZTc34Ze0vM5aejWHYOuyveIN6KRqVusmUuef7NdVY1VQ7fY526WkS1CMWY6bAXJ0AQxRk+yJ0gBWJCfI4XyHxAMgqW4o4hv/f6XMXlug81dgF735IcUxIiE9sYOdsL/bVyFJTLHay6W7zy+oppJUiyYkWJHaM8Yzdd4dLG9p73BHEEXYFzhK5DJJKY+MG//i6/+Cu/gl91CIkXVhvWktjOT3Gbjn5zgj86JXaBcR6zRpy4/5Of8Gf/7L/myf6MvsvzkcB+u2PaT/jbPcGtiWmLyBEpXuMHQbuJuBco1i8XLNPCqaVApkjoPJPskONTfvbmT7h7ec4r3/wF5M4p3d0XjBYnwaWZ0Hs8t4lbRcdroqyQacAfDUyygi63K98n7n10j/Pzp2xeOGG/vaLvlbgdSUB3cmTBfeOIV6touFoPOCKfvPNdHt27zfr4FsPRCavTI9Z3j9HVzsoHG2tgniFOM7vrj5gurzk/v+b++Tts05ZuOGWalKtRuHq248HFQz7an/PJ5TXzJKxXPail1gkOcR6VUKtZRhFmFaI4OhFmYJcBlAHLaOWKk89KDRz4lqFaNgutmGxV6/zYFvspcsot1CKVERV2IFnwC6mNGSjCvhKANONofi+EYJx/EbA3D5EDHvZcwNxCVQb0qyNjmWMVjHUitho2gZt+8PZZ9TbPf1h+b3ldq+nWuIVG4B9YBvK866GHt0arC6TyuwyC9LklKGu7WI5b/qdSsp/yf55z/ReAo+TcahapfyMzoeXLbbxAPVqXULl3KzXzrZLP4qG8o7JvZpt73iNthtlfdHz2mIGYNfcSfGqf5sqlz6O9KuqLH0xNF6xCpoUEdXFSBRGlB57irBpaAVxK9sVZ6d+CS5eFtpchUN0KGffljVBQvy4m96SU4rglyS01szw8SoCf0UD1yymUVMCbsniZpN74ogCLPJa00GlqqHfxhOR4gtZXV5dSGyOCHsSdcGMuh9XIbgYdNF+VG+Sb2VU3iis189HmHWt270jOSRXJBU8yAFNVJGI5sYp14RNwTkFdNjzkGgYi1fIgUqyFpS6BgJjvTEg4F1AJy+CSteElQRJhVBiTMs4z13rB+oOZW5u7dC9B0g1eJ6KLBDcQ/IxzHUi0bg1RcQLeO7zzBOnR6Jh65adv/5Q7L97mC3e+BCGw3Z4TTk7oT46JAXZpxo+egTXnD5/x42//K97//veZ/Z5V53AE5pAI0hHnxOX5BS+/9EXowc2Jbhhyf6kdx8dHXD29JI5i7ZSLOVytAZIC3iu9C2icSX3Hk/vvs334jDtf/QIvfu3rrFcnJB8Ql5ijELlm2/WcffAem9Ux3LnLMAyEsMY92/Hwp+/wzjvvsXn9JYbjDU4Cgzd3Rhpm4rhHXaRfHcM6Mo7XxB0MTkluwm0czu0Z9+dcXt7DPfCQViCecLJiWAe6/phZIlHB4ZjnwGV8il93rGfP9dXAs/PH3Dt7yqPrKx5eXnG+F7ZRcJ3gCVVbKoJZnFiHeS9M2c/ukzVoCmo/LU05ojrT+mFVi7/ffi+prV6zW1NKDRix4kpNYFc1hSOLLHMswhWt6Yaa70H+npKiW/iItMKhYewF5Feh2ArKwpSKm7VQc1GrDih4cSVIWm7zaeyvZRDy8wGIzZ86poNblc9vWFwO3AZl7cuaVF7EMt/nMgLasbQ8Lqdp1+8aYHUwn/xZZZ7NM6EJCC33b+oVtBPUNstBlvGmZT0WsNHWgbgxnjKvCgbJGnBaQJLaO63zUrAYi093U/9Fx2ePGciarAm7vMDFlFY2Wrt2JSilXeO6GAV92lFcAyUgRzVZMZyKin0W7AlcyK8hm14yNZWUO9SIJ/mQN3iqWmWxxiXNEcMxLcPRRpsUi8hdzO75kEJMBgJUzRxoe8Q2UaUj1WY7Li/5+UZDZFTRwkX7WQqDPL/NbwCLGwT5nBaQ6omHN/qLjiVggsLQaliKpmVzHoy6lGst1SSdMdma0cGSRaBa0fYCjjMgSynTT+4UpmJBickAV6WvolnNJQjVugImHQliJYJjCe5Jkl2wiviZicQ0wxsPrjnuNvySd6xvK3tybfuVw+0dRIvQdhIQNxG8I4QBUo8odD4gRPza8ezqKdMPEk9fGrl7suJ403F1sSfJnt717C4e8eG77/LBW28yby+RMCHOgK10nhAGnDrUTdy//wkvfuELcDywOurQNLKWgd35SL9ao7cT18+2BG/VHcWbOXAaI947OjHztq6AMRL7AZFLnn30Fk/v3+PWS69zdPcFnLcYDdcH0C3b48B7b/yUaTtx6gK9eN57+DFPLp8QVh0n+6d89Vu/gpwMaN+xISDjFTFekeIEyQLp+s0RaXdN3E0Ef0wMM6gFPHbrU+b9HmJknhL7/QXd6haTOObgETeQ2HG+f4aOR4SxY7uN3D+/x0fnj3iwvebhxZZpCqgow6ojRIPxSczH7l1XXZeDz2mKeHrJBavw7NSxxpMEJlWizqhEsw4lzd4uqfwtZk3OyMnUFYu3K81iFjBQyFILDYqgYhlORVFyRUstcsAZzVkmQSt4bqT0HWj2n/avYQBpoVOTsVYCHs2s/8AdWITtQls344IWweV/rh6xAB5prm2EYOVFxX1hBC1ow8rK+U3QXjvHqolLHfohf8vQR0tcUokNaADSTWZYPjuw8DQCt10KhGqCr7F0Zvu019RYI8pz3A1OntdCdAFyh+uWfy/NWapoiIvcKKCkjl0beaLmFv8LDDft8TlaGD8/2BIAdqB35oWqTWmqCUQQ55qXUwo+N4IS6uIb8RqSlyKAMrwtBXBcxhSp5Pxq2b9+2VzJXBgWE6QLetcckJZftGbTii1tplhRa1zSaukNIZo2n4GRLG4RKWMsc80/F/S9rGrp6Ocykq7mf1nep807C0jMLcPhqi9vpKIzWb6wEo/Nqyw3z4Lyxl3q26jPLt8tfvtanZC88dtrcr11qdfadyUbwNbDwJnGZMzP5XStlGNR8rs13hPRYorMmoG6xRVllorye2adKSJJ8Zk4rYmSEY0kh0sOH4Vrp/z4wQPWw2t8cxBWR4l5dqS5J/TRLG5J0TQTxCrwgceHiHeCd4kQPMlfEvpE3O94/GzP2ZM1vV8xx5E0jUz7xLi/YhrP6Da2brPv6HL0unagBEQcm37FPI3c/+gTXv7mF3GrDu0v8H6NIzFHWB0fo1Ni3ke8C3jfoTikj6TZ5UDzgHNKConAhBsGJDhmHbk6+4hnn/wMug43zYTNHaZ5x+gGttOOhw8/5EOfrK/Iqmd46RgvcHb+iHfe/AFf/fovMNy5RVx53HAKfoMfL/HjFvUTTjt0c0oct8z7mTADHeD29F1HHwLbcY92Kzq3sfTD0OODWR8vnsDl4wu205bz3QVPzkce7h/yaJx5eqn41CEhMotAEkKX96LrUAKo4EKOGRBLE7SmzZ6EsMMhyZxLEx6vMEafXT+zrV+TatfuewMDRvOFf1igoGSwkP251YxVgIHBhxo8l9N5F6tA3tuVZWY6lsX+WZl/0UZ10fwPhW45fWEktTJ5frYx2pYvYCnFVcCWZ3F4lGdXoWq8tKpCbdZSARbtfGh4efPwJX2zuaZMmSZlspznXGMNbUzHNtvm8hYoLe9u+S6PNfOWmg2gBW8s39mp2jyvWeziStH8/LIHyvxo1gW1dbpRvbEqjoXPlc1zsFhtXIKthWSFtqx94XXPpzL+/OOzBxDmSbT54Dd9HdKitSwMDiP28wvQskiLGCmpHUngwPdbhArl+7Rs4CS1XYaolV3U3MWpXuMWIWAvyfw59o7VtFAETdalyoolFauG5o1jFx+iNmMtZVyLeVyzLF9QJWhuqNSuJcva5GjUCgTKIlVh2vjwDnyDzYao76DZ4BmQVIJ8zmyky88Dk1v5rAE+qdCyq0GP5ZWmotFrdrLkYS39Cex9OLFGVwKoWKtWr8UPG7MGntNkksV9uNCZVSdOJOdzfIG3+I/cQ0IKoMu/i4KvBBMthqKkI2rW4NTcHSlFnl0mfvTBI0K4yze+3uFWQnIzbgiI36JjoHcDzvU4OnzAWvR2PeIVsyd0tv82Cm4GHZlTQkJCZEaIrHtHtx8Yr2bGbcA5My+DkIKZtkU8znd0Xnj69DFHD2/Rr15k1W1Q3zHcdqTLK5g7Nqe3uXp2TiDgQ6jdpJNaVgI+0DnPOF3h1yukC9Ykaba0ue52AglIWJOcR9IR+8dXPH32hPXJEclHSGqZLgJ0gSF49rsr3n3nLV55/Uu88MqrdMcbcCOyjviNuR40zujsCeueME3onIi7hNMBVo5+tpbg53tvHQ47T+xmejnh4b0rvvfOT3gyPmE3z1yNiet5Zjsr+yh0fWb4KvR1GxsTFwq9mDJSAloFb/0CCktWA42TOvZAkEBQZa/emgiBxQGkJUbAI9k1kPte1EZG2UWnkkGt7UUr1GrBiQYQCi3mpGovaNH6XbohOMt/Gh4rP4dGBZ7XoDk8V7zEKDubAAClgklEQVSVp618pSgyNMI736fwhapRZ8I54O9FELEIvyYCflG+G0bRmsMPXARlnH7hbe3U62eOwxgJKo++Od8lBqK5mSwKxSEvLzEYeuOauigcuEQqG/00Xl3W08a6qJdl/s09raNPw84zINBFTi4Loctl9fcsa1JJZSwyuVmTVnT8Jcfnsgwc3LOa9hfvU8FkZu4pC1eE5eF1LSKs4CejH0WaXgRNQAfSrLtAQdkVvObPHBa4Vl5r7vhU/GVLHf78HTUnYaExWeasNzdG+2vZkBTAUDZe6yZY3hFA6aVg393IZqjLVCwAZRUy1NBGK2jGYbco5qGc2Nkyj/YCvXHxwaGg8cbGhSJu22ChxRpS7uPquDVZ1IXTYilp0p/qU9Xei0uoWrlhlZxiWeoXJEs5QyyOwhWXT4luztUqLNJDa+Myy0Iw94RhLDFXQZ16susTjKp88nRPjA+Y57t89YvQ9YGtD3g3MmR3Ruet4mbwDoJDfbCuii7HeIjDe6tHMO8nvLPKidI5OrEKnIJF7IfBMcaRSAC1NEQfFCfW5S8lGHzg4b2PGU6O8HdXuF6QkNj0A9PFGdINxAl7VucIzjFPkShWnxzXkeaIdIHNybHlwTtBRyFOE32/JoU1fugZJ8/26RkP739A6hyjyz0fxCNBwCUkSHbTKEn33P/4Ay4vL3nli69zevcEPxwhXvFsIUQkOEv7XI1AIsVruJpIbk3ajLjkWBNIITB3A5vU87N3PuGfvPFD7s+XVj+h2toTIkrXLdalA6GXs2y8czgJ2SIgJrCT7RXz8zti8gR1TDhmPF6VvWZwr0J0Hk2WAYNTIpYWaB0NbUixVOCsPN6KYimKFQHKNJP581KhtMnBL8K1EeYintbiulBLc24bnNYK7xLRfkDfi8A6zJoSls6D+ZqiuRf19qagLcDjOUFZ7puMP4jtl8PApZ/Hh8pcXAMQWJ5zkw1VpWYBhMtICo+/ybNZ1hepMV8HPO5QiC0As7giW2WpnpABSRsACZhYbdekTCXQuhtqU7eWhzayp46ngIKba1PWoP27jifLCac/351z4/gcloEFdbUvTQs6rf6yFhG6g8/buy3zv4l6JG/cm9eUs8oCYo1wMvEVlFTzLKVkPKRFaKUSlJg3jbNaCU41ey0yMJCUF9BnBl6IsRlL8RO1L7f1S+nz6YRlvRYgwOK2uHleuwkawLU878Z1B6b/dld/2ij4y79rX0C7CWWZV8p97wvIAiwG1DWgQQufaQRxqW4orhKmgbTsVlP73N5vNu3nIC6taC1C1sLMQGBIvJKs2nusPcRLZUotoMVjQWEOPMSUeHCx4ztvRD6+v+FLr/ScvhLYnN7G+cjKKV1YIR58twJv5WYj5mdGOlDPOO7RcUKB3W7LZn2KzlZEyYcVMQqunxk2A5Na2WJJStd5a9nsrUlScI4QLHj23bfe4Stf/wq3XrqF72cQCMe30N2WjWy4ePoUlT19v6HvVuz3exQYup7r62tOjk/o1ivmTAN0IO6Y5IQUAvvoGS+u+fjDj5jHc0I3gHO4mHtEOMx140G8+bq9OHzo2O3OeO+9K25dvMDtF15jte7oVo5ucPhujWrCaTCQ41+A/hq3i3g9Ze4vmSLsHwTGN5/w7bMz/qtn7/BkGum8Y0VH56HrZgKCU09o9uziOrQAzGpVl2wJSJZwKsG+TzgT7N7TqTB5xxitToCPsHUQk2dQiwGYcin1BMSq/DSKQuZdJWhRs6WgqAWQWWCxnIlmsnBL4HmxFjgT7JrdpwdhQM8Jxkxtz7kmDwg4n1qEii407bB4qZu8uwrnvK4HPu9yzqcI6PpZBhQsmVYtT28xy/JLAQIlEJLl/nJ4fp2eFIVOaAW6HvDAAmig1ghIy3v5NGVneWYr+Mv6LZ9VF8OnPKqmg5d4CF06ri4emXx9ARulkRGH39fssQokzSJbDTmlpXHliXkwJVB7QSGf6fjsloE2L7UAgvrHDUhVvzp8Wfaubkj5m1GgUk9sXsxy6+ZGy4YjR4tWEJHTDyUt+xxDS6LtatrYU2PiEk3512bzNub2Q03+cOyl4+Jf1BhC2uVpiaxOTW60IbbfKhCo889/t362544bhPzpI2Ih4vz7wqmeu/fi9mn8bq1pD0v1tMJPpvFLztiwoi/5HaUWu5eUy/K8LLSQOj1VK8ZTg4tVS02NSgz2yhYYnBRcylYHAC1mZIsx8aK2FyLZFxx4erllu7vk/pXy9f0X+cbrr3DyQmIzKCqe6AOx64hExEfLXMj7KiUB17HfzVw92XN1fcHt48CLd2+D25q234OkIwg7+mEi9R6JgFM6GXAhWInjzhikUyHNyicff8g0Trzw2gluvcP7HvqEUzg6OUKniRCEeR8JztMNPYJnc7RhdbwmeiGIoxTTSepw2iFJmC8ueXz/HpGZ/miDTtm6E7I7yAmaAzJ9sJ84T8QRhgG8cnV1wThGVqtjhiFwcnrEyYkj9Cb4YvRIHM1NIJ7dBVxPwgdPzvi9H/yQn1w846yfwAkbv2KQic4nOm/uBI/Du4hzs2UQqbcxqlmFXC5vW1wtqEOdWVksQMtn875lEHgVgjNrgmFXyRYAR0qOmGN0Ys3wMRdAVTZqBgdV1jWtVTJb05xa2GjgxY8lVAulfV42chbW2tB6uaZSTCGKBQFoNkuT+R853fpQQaARFJBRwXLbEkegDVCoNNlYIKqMWTRbi9ZvpPfNKPnKUuTgz+XzMr2ygC3faeWLZOHYCLkb5vdaj6FimfJyyvXNc5vb1vFmt2LhP80J7UBZtBujkQPAJctztARjqzOlpnkfdnqJn2vBl/1bQF7zLJHGs1OsHHk8JWC6KOKfEQjA5wEDN1Hbc9+1G7WhELCN6Yrv7cZ9ysuqn0mzCcvHmYVnIVCvayCZ5kILdo75YQw4WXBeMd25jPKkvDjVLLg030pwTi1AVKAEcJQubpIHshDCEriyaMh6OA8WIQrGvAw42GdtgGKdlXKgcR/u4EVm5kECqVnf1nSYN4vqIfP5S4/GHYCjZGbYB1kfEbX553u7PM/iynBtK9PMXIpf3yw1QumoV1oYqNgvNa3LUYnHRXNfaMn+yKBCJbsE8htZqlJm60VljFLXzvkdSWdEVojsTeimRMfMPMOjJyPj/hEXT/d88+U7fPlLJ2xuWW1+3IQPQpohaGTeTmy3wvXe8eTJUy4enJP2kTDAdveEpJFXXjgh+MSoE9IP9OsN0Y1ESTBhvRZcIAwD3dAhLpu1kxI6cESePH3I9X7ilS8esd4ILmyg7+iiZ9atlSvOZYFD6JhjZHV6ZLEPwSNdYNZEmh1pnPAaePrkjEcPHxE1EdaOOBugktShvkNlwofsQhPBhYB4hwRPTA7vDQx4Z8Jz2u45f3bB+x89ohuOePH0hLtDjx+VOY6c73Z88mDLd+8/4F255PFO+cCd03crViJsmHE+IgQ619HJTOeUIBYHUtmkw+KK1GJPnFggqXMdTgwoSHZb5YpXzMkYccoAwOGQ3uHmlDMKhAkhJKUTwDtGJHfNbEiqMH4x+hK8KR5NT42q2btML5nvLC7FTAM1ct1VgWQsrJVSi0CXA1Ag2fIgy9gyrdvPQyG5mIwzDbYWgSrIipO5zC8/v2jhRTpp4QE0LEisSiVYBtFNYVGsxdlysKQ4ZmLNPGdxkdyYR5UJBaC04ytr+ZwZoRH4ze+FV9Yh+uX84m5pxi+l02LrGm3ut0ChZWwtx69xaNmhWR+F2neprHvh33ZvpbynBpyUqrr53vWo/Dk/qxSA+4w8/3OAgfrEgx8HL7wioAYZ1SCV508/2MAH9y9Cvrm2Sj+pPzJ50NbYtiFIA9JK/EI2sYhmLS4W7JVvVsBARnAuZlleAmgOh6dlvg2YKfUPaJGc6jL1n6PFFyFdgIRCUxe/Ef75xZcN8nzN6XJuBirVzJdJ6+dtimWAHIAJaTZgJRJHqeqVSs4rtp4AzpVOWnl8hRFiQZQpB08hiZI/XfoapLw+jkTUVIMNzQqRcJr7Wagx/lJgR7Npsm6KQmQYSLSwEmdWn+q+CJQULo2C9bB3TOqAgAbH4+sLrrZXfPDwIV987xYv3Tnm+BT6wfy6+3nk/GrHxfWei8uJZ7uZi+maIHCn2/DSqudk6NhfKleXkVdefInV0Yj4BMNA5wPBz0zbiJsd/crTr1Y416GaSHFCXLTz8Yg6rvfXfPSu8spLG4bbgc4NhODxQ2Aa96yPV8T9TBJPWHUMxxtiyKVwRUhjhBjRnfLxo4ecX52Rug7nPSvtiXvH7Eb21+BcIoRA15d03oQLgvMedRCc4DthVsXLCp0GdvuZ8+tL7l2d8eEWPtlFzuaZbUxsJ2WbJi7czCxrZiLeK3diwJPwIdENIGoWCHyOyPaCV08Agghg9QDECU4MIljhK4+nAyd1H6KKeGua5ROoOgMxJZjV2d7W5NhPSu9AgyclISbrZ7B4IbMwK2lMRXi6nPEkqe67ov1b0KA/BBNVYSpMPfMfKYXSMpooUXiFjhtBVGSVPscDyiNu0noj4AsQaM3TqQEB7SENmD4QPAuNtc+sGQGSo/+LbbwFFocSmVqLhhs1VAofrdc2cy29Aer6FFnRCNqW19Z7eZa+C+Q5uWbaJU6g4XuGzn4OwMnPz1bQRVYdCndzI1mnUS2aahmB5IqVCrV6oC7wwqRUDkiswK0AmVYel7GaMnZwzWc4PkcA4cHslz9TMUekKlDzFxz4gQw6NmiwvLRCFM3GbF6kINkHDahf1pcSwV+mkSgNjqr1zAVc7fKUwPm8jrmjoVp5ZckFa2rw2myBU5AsMjhr8UXrLd3KUkq1cp4mWYZ288imm/b7knJo35eCQS2IabV7i7gvfrJYNIoadRrrBrZpLfc9RDE8f87yIlmAwMHg67VWSCTV38u9BUElWMS0tnMzAIAzt43LWpNoQiMkr7lymxWZsiwSgNnAEBbJbt0JC5PRLOAFM3FmoKeWjJpS3mEaTVvEouvnXFbWV2tQrk2QRqwWgpJc3pk65fsJc5w5nyd+uLvCP4HgIeSxzCmxnxL7aWIGxtn81CEIl8MVF/sdJyvPrQEeXdznk08+4fUXX+H1u0f4oafrT4myYwqgc0+3OoHBMbk9kqcWxEHX4d2GeTRz+G635Z2fXXBrs+LuyYZ+HfCDR7ueay+WReDXhM0Rs0amnZUPH2dhHGF7teX6asdOI369wuuM1w2KR/sdaRUQP6HREfqAc7P5PEXwvSC+Q1yP84EUhcF5NDmeXV3w5PyKx9s9H+49P56Ed5PnUju0CEs/gM4ZdKsFmnpBJKIuIskTvCAu4QU6EXpxeAHRSAkcLSWqfRY6IgFRj/dqloKaHiak2OEk4B0kHRCniETroCrkfQMb8cQEs0Bw3sCClH2W+UyNUF00vUUeZ6BcmHRh7vCcLDuQRTXLIPOqokgV5apkHxwI1OZGlcTb71oBmcFHLoNd+WFtX1xuq8ZjLeeXZeApf15uKQ1f1yyYHaXteU2vq0pEHqbkoO2CjYwp2LoeaNk0z3ZV8GVKX25ex+DzMpTI/GINlRvnlbEX4b9o4PUB7Xg1A7McDwIu3/rTuHxZn7K22W2Tx1GApC45pDfmWxb3EJiUH1rnk8ftmlNqwGcZl6vbTwoI+gzH/w9gII+g7Lu6ADe+bwdRBU+TS9oKo/IS2knlDZz1ZJZ8zBZZlsUqQqwxhZsdrAG/Oegk31o1LPcXy0dPZMQmQqm9X6POy0vOKC1mt0JqRlV82flpzXoUbThbAFozUvUNlqkfCu+a+aALwltcBMvR7vlSCfHTrBB1yQ/8lYur44CZ6MEFzfk33A1FIGPARDVralLWwYggsYACu38kacoV9JxZa0SMSatNqgghiFg6Ys7FzT+NPozYCmp2JFADh8WVUZl6scBE6yenpJx5UEAVmKWpMMSU1zMwz8o8pspPEkLUnB6m4HL62m6e2cWJs8lxNHlOJ8/glXA58uMn59z2Ha8PJ5zcWbM67klT5Fh6Tk+HXDYXoii+Hy0rJq1Is2MeI1fXV1ycP2HcjXyownroOQ4966M1/fGG9foYL44jmfHdjnEameaJ/ZRQ78EnokbUd6zwRD/Vd+QISOqQzjI50hyxNAePMhEk4YeAF0ux/P/S9mfPsmVJeh/2c197R5zhDjlXZXVVd6EbjRYmApAZKQgGECRhlMwo04P4oHf9qTKTmUykkRKMEgRBDTSG7ppzunnPELH3Wq4Hd19rRdybVZWQFFU3zzkRO/awBvfP3T933xGUG+xZ+fkXX/EXX/2Gr+qZn5/h324LP6vKc/QSUTNaieqflQDUXlzsZMKtgncZ9GPFIjtEpYPFRfzzbF2dZoUGMNDioFEV1JZYp0oTr8FgZFGsFuFCD78UNcriXO+leoXCgjdQM6Ca1yggvQ0h+CTT7br8CgOlE2Td0p15LO9uxOnfhWLPv0PBzsI+BT0JvKev9NdEZoZBNptJZ8awUHtp2fqOeJ0V5KXRNgDBxf3lZxfyI3XuJJvE79PtitjDF2NzMTjkGOffdvGfaeBCNo/GRpeG1ajNoNNnsxLWKwUt9CyJC5n6HUpWIhOqny4fvoyvXayZIVffGfwujqfPLsIdOn33ap3F+MnM9/str+8FBi4USHdVXC90YbgwCPfT9ABpSfcVLJfffa8CmxcE06BNgyLXx0wLZOwc/7vqdI6Gl7MNl4o4i72Gvz8TFDtvYZjuXSHlRXLb9nfe2VSMc7znZQThLo4ZYbFLxRzReZy4w4WHHNKVNKO1+PFdyvziJn8/FHn1QOMs04LuLjxz784euBhzgS1NnKneGjWyPnxOXZA3cwKWP/+O4XneRR1ONLPwIEgHGoSC79ZQH78WcsLpoiL5M2RCzpvRgUCCQOcvnLoQk+g/73cNTd3iabZTTaji9TJOu/BcG2/3yrE0FhGkNv6DVP6FPFB+Kdxg3KnwSVv5019+wief33P38iOOLwonWTERTvXEdn5g357YTydOzxvGjhyER2087d8iDyDPyqEcqaZIO7gCtQNaG6YFSuHl7StevfwAFWiyUeSA7CtNKlIKZV1hrRyWQt1PSINlFZ5FqRxYyg2LHVn2wvpc+NkXj/zLL77k3z6+5a8249cmfNGMt0RX0xR2ZcRFJTrR+e5r7GiQeCseogulLoR3x2uIFAGdWnyX6PDo4GD6J1GzAecOVFu87LB5221BWFQ9HNXco1kl0kZbYcVbGysFdMV0tNC91E+T7DPGMarjTYFBDrRLhTpnEvQ9mLLlWnjM8s3Gfy/w+/TH5J3zv5Vh7TN5CFJ5Drk+dvAkuLo3IT+cPIgXOizkv11+fb7vC6V6ZXP4xzNxb/72NCb5aF28DWAydMx8zDQnQyHQdU63uudnn0IIswJ+r26dARPMDffGjwhBqHUOWr/nfq8T5+FKgfT6A1fPMtLsc84HEHxnHn/L63uBgbSixwDOn14jmXyYKwWfN5gLqnMM4u9cjEnSAS5jZ3m5qwUX17K+kQZSsm7pD0A2Og+GAgtvVDKUO+8hQgN5v4n4spyuwYSuGeCmP/IU+bEr5Hu94gXSI+EkGk9rlP7ZACMyNFcAAX/GMf3Wv3S9r747hpQDMj3A7HIYj3T1jOP38djTdcMtjzVv6iIg5jH/lhukf1s7YMj4WuutXIWiglW3bsHZ3hqfZVeJZjUqDhLgqo0by2kPR5OPs3sSsry15I6WRnonivi9WlSlSzxvOHeitQQlWY1OXXGJUg3ODZp4yKPUhdZO1MWo1fh6h7+Uwr/4q99w/7M7Prxt/OjDT/jxpz/k1Ys7yrGyLgWVI7oodts4t4IVY9EdDitLUYouWCkcWqHIkbo19k04t8bD084Xp52vCvxofeKPX73i1esbbo9wvDEOGXLRBcOzAFRXdh7Y1xs4rRy3QtkL5yfhN19t/JuvfsX/8O3X/Mu68YtqPJsDOWPxAD3biLP2ynZCkvvyVWSwYZyIZ1gob5XFQwHJSRFQVe8FQISYtPjzS6FIQSM8KOp7uWHsgLW+QnzOdWc1/3sxgpNg8c9BxhKZB6MBVmSkRJzbJkJg763Srd4AC7NHIWWiESAh9leC9Q4q5r0Ig9F8pWVnrN/3De8q4wsP5Kz1LMjUdNnbv5IejrzUhGe8FkmZ9pVcnPPida0rhmhiKOcJKORXLoVlDEsCAbl4FD9i/DGU8Yw2FGRnILoJGCQwm3XRDBa6AB7vjWq0cW3jKutOpmecEFPijJqfaddPF4CvPytc1HeJ6ycnbITdZVo6MrxA72iA97++R2ph3nj8TLT0Dnqcj5PL9y+UiozFr9ODJOO72dhU82kv9NLVwM2vRFmpYC0bklifawHPZTcNYd5c8cT7QHjNIn2GiE8q0GrUFvfvSXvffQRq63v6fZvEmMk5onl8Cs5Q7ldfnffz1VIb8uQ7lf7VQM2HJXAaMYeri85X8s/72JBAw7+jA3nhqXtt3LOBstBqC8E+ZR3oHs8OTSSsWKNQaO2SUW3ibak0AF12m2w9eyBcyj32Kl34egaRoQFUVBot+1UEASj5DxbhAqILXbWoSy/NPQRG1J2P9YuhpXT5NWqcK1UqB/WwSJGCWmW1SmXhrZ15fDLe2s/59vzAH7z6iE8/+oz725VlKRyWhSrK2d6g1lBWmiTZbaXsStuE57rzeNr4ZoNf1IW/rHf8bFe+2IXDufKD5y/4o2+Uv7aufHZ7ww9vCvc3hRc3Hp/3jA4418rzm50vv33k24fK03nny8dn/uL0zL/eNn6xCydTTBVh9SdXi9B5lAOmBkyKCnNXlp+He1zRWqyhVnckvQkhBkYba+eXlACHJdZI1j8Q0R6K8j0OUJ1EKjvVKnsF2FiipXSVAAKqFGsUy/N70yepKZpC+HaFzxDAlAn0wEX3wRTKCR4ujJ3cSjNpLQeoo9ZpPzfe8Rx0oZ9yNf5NrvwLYTFlFSRIuZAxcfwIkca6nsPsZv7M1zKh31Nk+cw4IX/rt39FTJzPIpeHdkVMPr72e7N+j7NOmK1s6ecQMnY/rjPSoyXTmt6jd0oItXymGYnY1bGXQ+FqKAzLvn5yjKfx7HOsFyeR6dkGdwRGODvOOVeVvFh7v/v1PTwD88KVizF/dx3YJdEiF3IPLVyNmMVGCAV2gSl0KKeu31Kbz/d1ce3rSZJB/GmGSfVvh3tozKNPtLWKqLcxdfKbM94tLNkhlGK/IX3cW3+udHtzuVASv877HWhUhImJ/x1obhD3pkhk19EJnua58g+vt2t/4r6xmRR/xNF+6yKyscGAwYXIhWlDFpnhbnf6tLlSdwGenS+9+p7hBZ2CUmglvAnuJiaUsGTp6BY8EbxCnKqPooWFVIr0cbBQ2AW/blpCroLcOyBaY7m2/kwtzuXZQW0aFo0GXi5A2u7rVIMI5nVk3PJSJBSkr5+Gk08JN7VYo4QSpRlvT5V/v/2ab56e+eHjt3z84iUfvviQm+WIrgul3fJYH9h0ozwviC08IOzNeKTydq/8ejvyy6r8si18ATzqAW8OZfzGKv/q0bi1nRdfv+WHy86nxfjxInwowmEtPO0nvtzO/Ko98fNt44vTxrMZz3vhuSk7qyv2INmqmKdKArDE+gxrua1IZpAkwSoElz+yUCkeGjK31DP9LdPszLIJjNdfkCSmCsEnyFUeFvekLz28sCHlmdYaYgcnGbYdYi6KKCpJrDv6fSgcUJ6ToDzLre7ubl2WdKGcewTCvRvk5FmJGHTyAl77YoQvr7WKTrt+tiTjUS2u39nTFsdEOKD5PzEnbaY89qykoLWpUOs4bwKFzt+WFlVb/T4tZa20yLAImTHrsV6ad8xFlx0iEeKMyY5nn7lMkrqhyyQf4+QlZZq2vz3J2wzVtLEOXHw4jyTBXJdZza/n5wiG4wwu4j6yT0Qmkl+shS4zbZrHrBUQGVCE/KjWr2HNvRWjf03ccMYxifvqejE3xyCRD05djvn1vf3u1/dILbRxY5cfkO5Uv+kyIZxc9G38nt+xaUEImO1RRTevIXHZkYvadY/hk1xmSuX00BJdCS8UHKR2El3clUzGL2tXiFn61mLSNFrvWhbNae5J8DaRO9nBz+PZ3gZVLCxVxrbua4vYkAFCJMCVTORJ/22nd8VKizpDHWFBZJxMskZ6R4yTqzvBB+9ZEBbs55zfC8A3T1cKGK6AxvjV+vw4+tUg0bRg5fumGHH9JK21VgNtLy5XdI+MAFwwxzy6iLcp3OgeAjHrVqGA1+kOZKYQ3kynMCe73PdWRaX2x5bgjqgMgOSKPpom2U6RzGARt3itUDCaGNW8Qp822FtDy+IdGktzJdOC2MiOmlKkeR0GgaX5uEqr4ZHyvfS8w+ntE9+cn3n58DUffPMzXhxvOKz3VKs8yJlnhSorwoFmC08oX7PyTb3hEeFBC48CZ7Lao8Z6W9kwzs34BvjZuXlZnnpGaf6s+85uDoAbinHwdZUOt5RnIaDrFCKzlAkppHOP9fXrWlxkYadStfZU1IrSbKPF3s+wy7yVK2fWKgjHvgAzjVRUQRJcC7C56Fb3EGoDZKeZC2rnJTREd4oV1lY4qPFojYNsHJc7ihRqSyAfyDD1dbqGDQZTfeyMToLu+8yfgOhL8Q7oNhgKdMhOS25B87U3u/V7zYKLgkFxr9XG9g65IdKivol1XazNQ1wpC2neqM09bRr7T3r3z9ZJ1fR94Yo1ZXgAgwTQyX0amziu38C8VkMPPcQCy86mrlt1eE5yZJuPlabMTmDWUxpD9uQQN3fv+8w4eLp0XAtW3fPUpyKIhhZcBK+bM82l0fVZr3Y73+cMLFLOaxClWwv5PfExmpNmu3GcMtyULMaW3RHHnMcrdUCuTRtz9Lte36/oUEde8/v5tzIKN0w31W9kQi02vZULvmvG9yitruQZ10/3h03nyXO8EzK43GxpXfip/ASWXQGN8Eak67p4TC1PqYHNY8H7d2Se+4sheic+L2Of5hrqX048MC32/oDiCzTd8Ik2XflOJ5+fqlsr77mPvuMvdf13vq69HIy/+1f7ppoyILg+cQg2mnsASm6csCNrdQCG8wuyc6WKBkk7yIcdm0eZzysGd5ICu241/H3JDIMWQiY2oVhAqL2Pu0xFZJq1btRIWFM5Dp0kF4pWVf1fwiBjABZza8aJbjVCHs0VlZQJ8AXIA56qsT2f+fYMSzlT1idMlJMV9rLSVp/xzYznRTkVpYpxUuHZYEuvGwatefMexvoBMC3UUJ7SNjaL5kTSxrE9rbbSc+4vpta42HuX7rM+/Tb9YkCVSmsltsHmVEK7hLNDIIZFlmOu3f8QCi+tq3wj76VCtCByb02Kjdx3kYUQHJQmNUIfvueaSTQ96nlx9M3cegB4kj8un7JOybB2459djV2O0/Vx8x9JROzvN7fy+/cvThbnazFW2X7JwYLVzBoYMqTbk8ZFY7WsJ6KhZCUfP8IVXYnqCGkkFWyIGRv3da0SAix6DYfJjR5jkmnA48HjZ2Z+MVNSLr8b0IdrUmJL4ZsPI+Ex6LI4AE1fKLMcrb4O8rg+JZkpMXlX+uMPWShRHTCHJd6d1m3IT7eOxr23xjCg8xpXe65fOHXm5DH4Ha/vBQbey/JU8YEJhObH1stsjkCXlm6PHND0AEzrZFyPyz/e0VjCO4u/D0DfceP9EV+Io6XvF8kJ75t7nMZtKRfwNSqZuSyIPIPcp9N5bV7w0/3ajOCYQw2XG9wBtXgt+fkb8VzuchqCvD/TO+OTO2Qe4AQXubiv77Of7P2/z+9dyP135yPWdb+uXpBgwNIyR8haCUaECeJZG63L9GwH7hUF0svs4Zg2u+XoviQk3LMxciHsXJmLtn5/vgaqWyni3o38LHkm+YzS0ivROncgJZ97JXzxZ98EL5/rnktFKBGO0MgfV7FO1rK+KH0ERYSlLIgKGwubOWBqKMKCibJXz17YVTmXlV1Xqi1sugQQmMhQXZHmHGY6blxbFMvCLBIWiO3x3SlW+s6SmITw+9bR+/YvDlabOQejNqWpz2/Fs0VazgeVahPtsNXoBeA5Kh5ecADWmvsyXC+kTHCvnQPUwYlxj5q7YT1ttTkPwdwWVmsUYFWoFunHXZbMGrswAEGOx6xsch/EPrGMd/eBmsYvAaoM0IExeqdMckxjPQM9i2be7wkI5uuQ9V4GTyDBu5KgmK6c3KA3d23ja1wCeM9ryvm3nrVgfc77hI1buGibLK4vLMbQynA+a4zdBARH7H8MZ7/J3OnpBexqNp8l77ujEFKEe3h4HqVrAuH0Eu1zYNOUuo7sNz6d7HLd+xRJ32MZAiJC0d2I6/ohnyuDx8GjieMuDMoUjPnmd8n397y+FxjoxIVZd3VGbTzAdyhsv9dLhdzjSfMrD8lTmvWF+1tujkv0ydUExg2nWyuR4IUrybrr3dIiVf9bqm+eouK7Tz2XvrvxI77c16rvpMtN262EevEkbgxYv256O1poUgmlMCxwocedpgF7r76eB1J4R6m/C+6uxu93uQssx3Q8S95hnkKIFEGmY3ONxnM3AbUK6pu4xdg5T0BJV1cL4JZdCy1clRrpPyaXFnrWtHBXMFGgJndOWEkyASuJYkeWY5ouWqOot63t4ZjwTqTMUWCfPVuzHMniLnhcXW1HpCLiKt11sAD7JGTMyYoCS3EilqHOTSgCrOzqzZJMC60UmhaaOOQxKezmLb4tBeDFfA7LIkGbW5k1kAtejCuEdAe+Vphd1+9fF79LAuXnCdEWnLwbCteg6U41D7nsePse51o4x8NwN79SwjOUeyuVehQoyvHHvRq9cFisD6MiEjUJYuxVGgoBXhuLNFYpnFvA0Eh/cFmeXpaGK7M2wADa94jkeuzPPY/TpOBmEJGfpcaZ5FTKieH5a0NBImFF5vdzD4WAylbG5qZLxuUBb9rWHIj1Tq9hqeaKmYOzyW6ytO009591kmLKYiFj+7n/BCKV2wneIZubEIU8/L67ey+Xj3VF3r1RXdbKFGv3a7Yp22so2lTCk0LN/SsZXkpFN38nTpX9Y8IbYhECyZTw2Uk23/1Izw9pLtC9WcH+74ZIHheZSu+tzpi6L7lLOb79GpcZIr/t9R9RdCgE5fykMzpIZZ6DnguxDzbT6OSiuGCcXMoZIwHx5bWucMXYGPHh7B24OG5GfIZbHXTFPkinTpbR+EprAkXci9Wy3fEUJyMIcekx6Ag7yIf5vP16eVsN0dLPkudKUNE7o/VFNo9XTvh4QFdsI5wwBsum36/G7Xq1vE+Yfxc4sOtJyI0gF1OZ3dxSHiVYaiEw2vT9IqV/p1qL8FwSz9pELAM0CGYJRKUMN34Aqc7+DhKpdddmuk2hp3mRgrHRQwiWFlRjbmqV/n+pMoSKZPaCC6ISsky0BYd5R2Vzjyix8TWEcoCSJFS67lcn6ElDZMekYChNGlYMK4VavKb+zuBLVZUIeExWSsacQ3g6zrK+Fjvg7s9rQZqDsZby+ds7S+m3rpXpnBcfWQg79fS/zZQ9dEINvZCpgZKyLubQMKrsWFPMvMOiSMUTOCtYCx6ZW8wtwJ21FpPiniOkhe5UF1etIhpNZWIjFuDQCifzVNHLrR+KzxIASB8CYz542ofd6s9NMhQEMkop939dw9gY26xyFymNg8zR6LHkDhosCIR5C/0GhwPDwJr0/dMBRobXYm+IOWhooXhaLg8ao3Sx9AlzsW39u00mr7BcjlHmJl2USh4WVgcgk2Shh2sFRknglIXTYdC9fDncloWJLgBXHNDTYuMtDYU8b4lc2/0aM+h7vwk7jLA2jrn26OTEGP2ntercGSSeM+c35h6gTd1053v8PV7fvzdBTwPMxRmLTifh0Fm1xE3GaKZVNQOG6dnHiry6aG0Xwvw7RrhPxgXwmM/TBy5CHsnkDYScSiq/Y6lMVCcgZpHDnrHdUATpjrUBBIaiTPdR6y6irqvxa2RKnA9f/Dant7z3NUCCJEubEZ+8ZKfme0PR9nSaGcZ+1wVnoXQx5gkAcvPK+Hs+rg/t4DNcdrAM964kYWxKpsl4PV59Ls/vttglMPJqgklAzG52bQjmEGAiZXqUNkS1eew4ha6vi5mo2NgtY6xGsyWUd6HfcVy/mGdALGEZFmksEpkMAUTUQiHhBLpMXzUNImTW2RftzYtMVg9dL64sd5RNi4cHEHaBk1Z2vMrmO3OcQKkXRGdYT7mWY234thLIst4p9boS/I718l7wmORMI7Mo/FW9FwCwl+ohgubFm6q4ve9AKhzYUtxrFACqVfcCNOd5IRb1Ofr697BBphbbpD8zLNSyGFXxfbSi7o3Aa0wcxdfs1rwNMtDd2NImjUqotLwI4gpSIJv0jB2ULt4x9qPuRU6NML4Q45bzNHtoJvd553N3mdhi7IPE2t3NuU8IdvsIG/jjpWp2AJXNyJJwuBQJgCXdPd3Bsg6ALDNw7HorgZCO9ZTQ03KsJv2QHhhm8JMIYPaUjjVu80W71R/nawk9ZgCQH3dlxAW3wya5JtKnpUux68v1ORpHdm90a4y6MKnYh07pRNB39GXqkGkiAO/IOq5n/YZ/hwqZXt+/ayEMQJCDG2jJ7zuU9oU3IBmPXeQOYZEPdmkuMFxsEoMrF5/2X+aNkufpAzhtEL1qHZmWHYIXtwEixQMpYx+TliWuxGk0VXf1mlBt98SCirN250mbkF7G4VxIBcv+YhXNuyVv2sfJfyQbHDLlzXqaja9umYT4O3M3K+3+mkgoaXV0oJdzHDsn7+fiHq+mLK918X48W/QomNOG0urqFp/NgtQGs97jCDhgyFHEWxD3O8qxN9AQXOnKx1xJaJIHjaaCsEdCSgKUfFZcefQZFJpWnORkiC2494VY16WLZY3viuDlccWFqYoz9EUqohtJZlPCFS+KRVqXaot2uUGE1MxXD7CghuiK4KGBXTWEQaHqylm8255JAoBrQCBh2Ld352ue37RggtDZS9WKp3x6caC986zcVVxizDfMGsoSmRcW42TAjteSiJmzxhllxYl61MauQi1QrVENStQCcH6Zh1hK1MtXmu/vplAMES+UrLLG3DpYMKteJVK8uVLSSC24BtLJzQU1YZXKQRZMdqoUVBobRqve6hgF6x6rWCt1UtCpjAOUtvSUCpOFmt6pUEPN/HQ6cZL6BpvAXF+nyYRJqS/dkM5iQqkYxHzeaNWVtkW0OrwAnh7q3k9rDhDV8M8C7ITE8XXbwsgJL6pJeEalOdAUxXoWiaSoDXKhey9yj1sUVfKY/lSJNYCEVAekdj0W8/Ck+CJDKXJ1XPO9ZClBck+Mueockn7S9MRenctC1+WfV8DXpv8OGRh7uLUABbFpmq+DLG9trU7yOMYuQy4XfISQ+QJYHQAw5tp6af3fDw58vzDBewVHPmwsypJKPpRtCqDutro6T3fn5wrOh85JiHNfW5rzreRnkXc8lH4oU5mVUAo6mAGHaPISLC4tMcDZV4BwG4dSq7kRhutoWAP0okWtDYU733K3kLrbSeJRbPwpvH/MLs5zMRDjWnI5Zt/Zvrgj2BzHeTO8czEuwgJ2vdi+A4jEvCZ4SY7FuGRunSTmaa8el8dkTflU79nmtHsXJFKGwntUiiSui94HLTwJghYD8fzyi7LP4TJ2+BfWuqSAyPLDrrBak5jbEu7PWAex3tz7aKg25wlEKqNG/FokO/DFnKdHgvD2qEtN0eRGxPAWj1lXEXYpNPF+CFE8mYpxFqJLoXFZ1CWUUwpLIPLEpgmzaRlNE3BlqTSiIp4s0eirYrqDVKwtYF6AqFFAd1R32m5xCgVWzDa8zHR6WZpXcxRPL9yp7Gbs5mTPFPCLNkScUGgiqDWseHMi2oKWoytJrSFBUoVBFS99bJ1HIDTbyOI5Lo9dMB9kYxd/hiMFFeXOvJjUQxO2zDbREsqMZLWS/+0BsHjuEYqZlOQs4GfwFtNxmRWRhlWcfy733udY+nclQzppLZqTJJe0jM2mWh0MpWq+x6T5fig9Rt+6x43m0E677eDeVi9p0Ho4TmI3JejI/WihtEDcANcl7sH1hmVmgSVlGAaJNUIhjGdNWdRHq5vQ1+ufvo65PmQCFR10ZW+BEYsYcjO7PU7nlQR2CQAIxR6IySwzO/x9v1R6N9Ktk+ef7heDTuaN8ZyJ+HDpqctnafxer+/PGeiKWN5dgDEQXZmZo7zZTf2uRpw2Q895nz6+CBswfT4BiOv765cLpPme43xj5mOkBRT3odBT1Sw8Ct18hUSYZu5CFGtBjRjxyIxrj9uS7qJ8d0VeKtHrx+0Mc+Zneh8QsDFml4P1nuPHcZcEzRz8eVHNYOE7Xhcu6CvAkOfob+v4ypV3IwWE4shWAixIfEECqwyWePyIeLh7BFwpl2DzS2w6w8LJkKEVSNfk7BWwTi6Mf3HMzP2QDBclgUu4mBdVeuqgqrupkercAXXxaL2N9uWYuVxV7w0v0DKuqcUJhCoOAFRpKCYLuy40Fs4inKfZfOf1Xre+Xf0+AaxQRvbOMl0mpdRCyRxIK1xNaJwjMyGUoNKtVZqn7Yk2GoW97VQxrDjQ2s19JztGtaQ7NC82ZKBqDs2khHWuGAvYAWP1e8g6IPEsDQkegmFqVNtpFuDACnDwngjsiJ45slClsjU4WEVkdfJmdcPnqQkbeCaLRhglQKwL+Mr1axQio9+XC/nJS8cVvk7Dav6gh3EWMt3zGpRLhGi9aNqQSWYu+NPLPECJMJTRJHMsgHocPwy2SYKJdE6KxndrKjqkf08L0DKbQYN7oONENax3sZDBeilPZBhVXc7NBY9mOdTHY95f6aFNuUpXPUM0O4AWDe6VRfnJ1h+eHv62K01rTCEw6xa9e6YSOLisEUki5xhPEnD378Yop3ySCR3MID0AQfc/d8+3vTeL9X2v71lnIMdiAgEdseSjXECrCQjkqNt0fJ/W+DnddSJVuFRM/WVBKuPSWL1Sru/9K4oEXStHtw4StEwIUCy4MbG4o+CRVUN0gRpWpkik3bhCGcSYS4LkhZfCeGdxd1A6oUyZANAw2CyKG+X4Xr+un2++i/GMNj/zBOwuFuh3ne99AOG7QMO8WRvTvMr07L5GvEpcLHPxdtGllAGoxN3wovFPBkhQFXf69AAn4TUA9xC0Hg9161+6G7kXYpKog+AQol/XLCBLhgfy73yuvBexaJaTwCI5ISm4I/VtEqoV6/VrPFsC93SUmUcQgfH4vYpSKewsPKvyRJmpKn2NXQqceU6GvulTbTk3oRzyXPMy68d5PYILlngtkSzlLYHd4eFRf6OCbWTVSGsCutBao1KpYuw0isEunrpZi6HNx6+Zew20FXbJnhEFQ7pTUm1HizpIEMPZCNVrB0iE/YrPrFtWft9KoaAsUqkqrO3IKoVFKqYnjAOleLniQ1sQKTw2OIv0mHn4wN0Nn4jTxvqG3AJp4V4P7BhziT1jF8elyI8VPGcRTNObmSDpFckmN769M2TRKObAjQgJ+CMMGZP3kK3E06ip1S18I3RSbtY2sEtR6febskaquSGlg3/g+kAHEEg90WXQPEYpr5yo2UPSQ4gOGXMt37tMF0Ymm+sR52nEnEQJ7Px+l8ddf8wbYHrNRm9X0j6fZi3CyNNzRCXI7h1NwnLPBJm0V/971p85SQkKJp1pkxfqvbrh3dd/ZAvjcY+XbnfJvTBGcP55IaRwxur7zjs/+Pw9yJUXwMhCoeu755gWwjW5zX/qWPCplHWy3PuGTY3kjXUsUmfc4xMAQDN+JiONa8ZL5vfwnSp1MrtSaKhId0709fB+THQJXPr8B4nxO75ysXAvjssL7tMFZfr3+y2s3/rqYYH5jmKDjNWMGVFiNo9NxT5F8WQCSqGQOukpoWGAg8wD95BQgIJQYK3Hbq3/7GQlPNzTmgvRZgH1ck9mKmN/njbJJ7cA3EthSICMzs6O4TCimNLFs7jgFtVQWUFS1ZUm7gnYdGVj5WwLT+IcApLQhgt24vz+mrX+mA53Gc+WS87H1XxLCGKYgIbiWRz43rSK6cayGIs1rC60qn1cPMNmG3vZXBlUbZxb42SVRb20clWh1gz/iPMDmmdKePaZ7z2Ks/8pOa9LlLcG2DvItPjXTEAWkI00YFR2CsIqBXRht8LRDtRFOWnh1Dw8CIoVbzV9aArVuQ1Nwt1NY1Thk2FA0KYtJJOsF3rl1i73JhJit4Bl7Okkn/XpkTEfEQIQ6HyNnHXPNnGLN4rjUnBFb9kHYhLblmWMc0dMUaXW2qXTcCKdiohj3oEambT/WF/qnovMbJHON7BOIh5rL3/J3gLp7WjOss8xmxssdcVNcI/8Zv2OQn4zPJB9+dNi64dM7+s8nlMHAEnAlFwnD/9OHoPwLkiCwm4QXylzS2Ca525jj+SVLuS2P2d6foCe6jjCENPhv+P1/bIJJNmuVxqqd0fKlAcb3+mxRiPYKJenFbtcMJN7YwwU04BMryQFusS/ut/3ocOrc0yhje6uy8Vj0IlTfWDjuaOSnCsVifBVsmddcElRqLUr6kukPQ1P7uf36e0uxIcgT0Xesxj6+SbewoVLMV1GA2j4epZEBReetZwHkYyD5uKcYnbXrrHf+uoSb4CWcKfOKUNj/sc8zgVCHGgFcamXf20I5QIQZPy9u/ZTjE2hAUfn/myecd6cgX7pK6THe1sQpEyA0q/R46TjhuOnz0+TxqKGV7+L2gLUnjmWy8ymr/fCUqI+FqKIeUXDXZQmylkLZ5RnOVDtSJWVU3HuQE+pjH3YLba+fsde9YsSiqUvgOmO3jeXV59nM5525lieeX0Lr28qf+2l8ofHW6Q0frY1/uUXG7/4auPpySsdJmNaEJrtmBibednkW3OPQpZB3qMojSrhiXGGemsSS2mhcnDviYGUqB+vkR7KisgCtuGOfaFZBfVQh8hCURlLTw6oHfqaaraw2ZEV9X1Bhmdwb405QDJT0LDWU8lPXl2JtXOxJ/p+nQR+f4WbPL/TOrzlIrwa8thnN/aLCWrCbm7xCqmoWuiyaBoVpNDcLX47k7IyV5Sa99oiPJZ1N6ZbzpJQQnLMBwRtKYvUkOocAIvy0Cag1S5kUucUdfe4TP9yDcZxXcbmhjKGfWhjnDr4TMs/DrKKWQl5bBOuyk6RMb79mfI8Vzovx4s27rurlJw31yXpPU7PSHJYnIMQnqGLMALjufNc83bG96G1erm23qtY3v/63tkEQ2HodB3pE9JdheRxk3K6eHv2CuRND4tujq31BTcJ6o7ODEbAR8diiX+pcHvgZGZoxgOYRJ3nrpwEuR5PmxUisShC0aqHDBwRuhINkd7/l5t1BkOzcg6VFcMY73cgM+55WHD+/lgXY8GO8ZN+9feiEaZ0xj7Ghvdc0NwR00D0IXvP61rATeczhqLk6ve+Pq5OHExYjfnsGPF6k0uGY8JS6EVgfEzcMA5Wv8TYGV5ciBbKMxQog3iZhT8MRjqvSWQA+U8nBMcYR4qaSpQb7rfq+0EI5K8uDBzU5MBP66PLHgeVTQSkRVxVaMVTBzckuhcqz6JerTKHsEWMcwZfuRe7Tz+Fk1+LTiLLPZs/ZFJu4QMGlOqApTVg58MP3vBPPrvn7370ko9W4fUCL+XAKgfe1MK/ebHxf3nxDf/Tr97w8683rK0xSV710UVCo4rzIRqV3YS9VlbBqw0Sitgs6nlEDNXMuxCKRHGi8AiYcwqKKg7GVp9D9o6LihzY8ZbFO9EOWQ4OMikUWTFbObEgLBRZaFK8WqJ5mmkVVwjebMkBo+K1/Il10aN51JAT1pXMcCOlspDx9wzgsusmqSRzK8h0Lv9nInF9Bw2+tuP7zdMmseahVvMQmYnAngAkFVNs5Shg0RVmS3Kg12jwcGWEjPC9IlgvqS251kKvtxaev8WlVE1rva/V8K5k2IAQAF1QDXkxUkgn2dC/cilXLOP5FmM4ZRxcnCNvAyNjdyMkM5PS5fK68U9njkCen53MirLUgRkS6AZwjTXj5NZOwk+QlyDoYgxy3SRwzCGSIdJ/j9fvDwbekdcxWblo+48rV+MFE3b6tev0VIiXCkoCcXkltnZ1cWN0lkqlkEJ9AgT9rHKpyNPJ3F3QM2oYVxiLrnS+yAAQYXOKubUQk+n171u3akVzwflim53J8/MMdv38c0b+09hlR66xYofgl3Q3RYMlmwVRns3GtF3cjkz6/3LDjVv9rpU1AMt4Xc3b/N18rPl0lhvF59/vO4BPFIGRXgpUunXpy+3iRP2HmceZNayii0uJjBUibr24oRHx7G5tSAgvm+Yls9DFVX8fZA8LqAQAiXRGjZ8DuIWina5v+YuMeegGoDgwcJ6AsFOoCGcpnLPQVV9qk9C0vKccoxj4ZDnr5bx0+NhS6FjcnMR5wrrdzXkzJnz+ybf8t3/yCf/lx694fXxFqyCtcdQDqyofWOGDm8antyd+eHfiv/urb/nzX+/ePEkEdgE1tHnVCJWNhFJzmeksmWyteR+F5ZYjCxuNxXYaR+crCOw0hHtUKtZO3vmSStEKkmEDRYqylB2pCyIHKMWBmwhPHKi2sFD4oMGDGo+ysahRbEFNWZugTdmApRkPUmnslG7shMcuFIF3DQRagu3Jkps3hfWNyIUi7PI/frHirnWMnq1DkiKNbGhjrdKrXcY4kh5Ld3E4KDMJjulYo6Nl+KWMSjLq6NNkPY++ibEoXqxNcq9GKKn481meQ1qUltZe6ruv495z5HKdEqEBEuBfyJk+an3Ny0DecWieG3qtg6HLJ/1u9PBAzItnD6VXYqobYcP0c7kR3QgtdExmAnSAns8U1kb3WL6HmAgjzIBNs5Fzbm4EdAAQY6z6znm+6/U9wICNBQhMw8VFfCTbY8Ik4a4VyBVIgBjsvQvnkS8fAydM6CndUe7OTnBmHYyklQjuhioMSmV8X/XytgJJ9Y1HCYvFhaCjZB3nV+/V7vvEuouS3nnLvEpcy2uGkvsOXfq+tL9c5CMjAvc6dOs3l0Oo96mK3lgqac3FeEbntRFF0fdeu0uePg3zfH+Hgu/fmQRa/3vS/G4ajHtKwmKmCk3z7fcq47Bpri+mL67Z+QIh6DKft9KiymgLPdd6BsJIebyM6RkWMVY/V0No5u7gRoIHB4uipd+zA4CK58O7Asq/vfaBhAdxCCcjPAG0EOBDGGd826Q4CLBC0wObrJzRqQxyDspl57eLuWq5fwZgktgT3WpKENYXSfjnoykQBrYItMoffvjE/+F/9hn/+Eef8/LFHcdq1Kcnnh43dF9ZjsLhqNwdb3ix/IjXy0s+tq/479Zf8y9+1fj6bNi60PaGFKNoRTelHgyaolJoVt1T1QWwIm2hnA/spbHKERFn/mPwvC8shwPItywvjZcfLRzujtT1AEcnqtU3lcdfPdLOO4WCWEEpSFu9vHNbOejShf+jKDdtZRM4F6MsO6KF1nZOTWj7ip6FtRonWdiKIK1iekZML9akyIKUQqs7lz0KEniFzHuHyJvTOQCSv6ndI4iZy7bqqYuS52qtRzyt75OQkQ1oDmpbWKseRw/vWQPn1dgQ3eE9c5Krjr0XANurh4aHUouT9CTl6Q56cLZ+harZ3jkyiNQ9Zy0VpU7crRwfLERxgqrJoxJrpOfy91CegyMSqPX9ka6/SSf0a837JmStWL9mSp1+XN8gDS/tHWClA49U3HFdoHMGeojAQcZYB0mUT9kYBM/uscs6ILFXZ29CS/n7u1/fg0D4HoXRF+qMcqBbhInG2vT5Rb7yJAyb4bHVeEPekybXlV2S42RSbNItr4Hs8h4i/mpxb6lULtzgk+LzG6LH3yQY2hYWS198Axxk3LrFyVu65yTTSnKjvF/xv/eV7qyL2Hqb/p7nIX8RTArSKXbJe7g48ZiCnIbvjFtef/V3Lawcl+laEq6+a0Axb44JbLpwyYZQU9ZHWjLfcZvd9TaBL4011JV3SDPBdZuvOOvAoHtTpvGdyb05XimUI0LIHAoqIWSTMHiRDpSAVi8eOe84jDQXzIZApA9WopWwCU0KBO1rSwDVx9JrHPQywr0y5bwGZiCWoOTKozcGFCIF0jLmLQu0jQ/vHvnf/8kr/skPP+f160+5WZT6cIZTReoGPGP1FvbCYRFeHT/C7JaDwa1Wbpav+b/9YufLRzcCZBeqNurq2QTZvryau657p0iLgkV68Fr6CcZ3OB93jq8e+fQnL/jRpx/x+Ycf8dHtJ+hy4Cwr+7rS2sb29ht+88Ff8le/+CVfvXlir+qrtilb8TDBseJufzHQM9zA67uVT+7g/tY4LEIphf2sPD7Cl28bv/xy4ecn4evzGZcWK40AMxk4soalLOkcnGlhT0J/WIxw4QbO+zLIrCJ3K0/exPhVQg5qeE6bCZZdAi1ggeW6j1bFeQ0LomAHFWMfD2J06146x6UhH0Pk9gqNNu5ZAuwkMO4KLDpKdlCf56qx7roHOFGMMeRNeJSxKP7EAMUJB/rmDQEo4T0Jz1c3XEJX5VfNiJ4XKUcY55t0oLTc257B4t8PnkA8V3KayM/mtNLkaV14D1qfC/9srKW+n1MRWnQUnffxNcH+O17fgzMwCeyhwzviyodJ+dxbsZoxiGhxbMQo+5BOiCq3xCi+MykQM2Byq0WVLouByOY26dbs35Rw685kqhDcnSvQ9+LVIAvjelOMsitZKUgvjhEx/UQbUfJ2dk+3eI5UcL2Jz9XLLiYzbzlusuMW6cdKv8/c4NmEo3Ur8+IV6HIAkzzXfOyV8u639PuAGRn/fifhcAYEucyERPAXHBLGbRg+fspI3eox7y674ns1atMXn/kWnARJYRDrSFqk94WVkjVFehVRy6hvIu64Q4seE8GYlhA0nnEyC/QYPelPfQlqYv0aEjwB75TncfQh/ryZT661wc3N8fIL5c8eGAr8amNOOiIJ4Tgz3vDjshOHn2NBrHJ/PPPf/vFL/lef/TU+fnHH7XFFaDw+G2a3rPICE8/Nt2ZYPXMoG6/ublH9AX8PuJEDt3zJ//DrB375rSt0XQp7Mw4sQ7AT8p9sGb7Q5MBmwqEo1Qq7bSw/bPzpn7zkb/7or/OTD37M7WHleLwHvYFt49FO7EtDObIe/pAfH37CH7z6N/y7n/9b/s3XD5zeeB+Mw65sGLtW1uPO4cXCT14vfPwCPnp5y93dDbc3NxQpLFZppzOPb5/44vnMX3wo/OuvFv7lN8ZfvYVvH9xQwIy5EZlk86cc6/6rjd/7PEzyZ5ozmazElmunf1dDsfg5xMZ+isoaqNFrOAgW8fWRheRVs+XSlDAPBVzI/ugjIJaGVspTib3h+yA9f3kv6W1S8OqR3RsfBMPUFR24WgcZjgFc3sm033t44XroQrn2+jJ57waWNRpShzD1LOzYwMdHUM7SGBkbXoxsZHoYYg2hYlG0TEgwkL0+8g6a64aYJ9d7cSy5fftDzZNAV1iWB+Yefo+8/T0J398PDOSN9LmJG50UV3djVY/NWVcG0wSRAqtdnO9SWaelAu4xSCJJ2nLBBmah54ajveWpo95UyjFQDYayzMn02e4wROYVJNCSGQqe/iP09sviyLWp0Mt6ptEl87Wn8SLJNpMimPpyDwAzb+xpjIBkf193RfRXm/4escF+zvn4Eaier9yV8qVUynG5WljdtJ3O0Yk51+eYnyH/YxdvjiYe4157/TG7/LrFZaMHXWyg0LKSsbu8a6GaeJ6z+C02hhWPBRMdd09qEJw86qUhS7zkbQvl2gJweiw/BKoQ3oBKZhG45LB+v8LldHa9K+IAVz11qnUxUjApVCnsCJsIJxHOZCW97Bw6uUCvQN6oAZ8CdgrnXVif85y4SDTZU1IhwMFO/LNPV/43n33Oq5efcFiU+ig8PMMqr7m9NyfhFTxFb6tY29ntzGKNV3d3rPuB2/1L7q1yZ/B/vvmab76uyEmoB29WVAN2aZHhFRBAjcUEbcWruS5nfvh37vinf/un/PTjv87h7lPK4QaxMwsn9qfGeTux7M+s0lBdkPXI8YOX3N7/PT768HPuf/Wv+Fd/+TO++cqQZhzuG598vPDDVzd8+uKW1/e33N3ecnv3mrLe0daDS4Fm6PZMO77lYX/kRy/f8gc3z7y8WXi9LPw/eObtg8eKNRjfF3Wfekjt6r0suNOZ7zLhtFTs13sn1Ej1+ZasAghJ73dgRnhBBJw4GJ+bORE6PGPNoqdDgt3sdtjaZB1bOF2N3L+pVH1ZuVs9OVWxoUM8uBz11tNgzbk9Utyvucdy7Bl9lmMy5GXvW9OHwVNxR6GdlH8hX3tJ7Wm4SWKxRENKJ6KW2AKSG9eMSqXQaCos4RmuVt1xQRZRCj+eePtrsxbF6No4V4/ZBCAAegtqu1oPk8QQCFnSrj5LWR6ewD4m1+f67tf3b1SUf1qgJVLxTA/REU1qxpyQMQs9baKnURADmFdoOQvx9z6EWN6QCD2eGQuuWxO+AxDJlIv4ToKXJBRKcr8zZzYWc8BPkyB0JOJtqUhdlaCGNe8s5zFnXz2iHvt1BnkI6SnulZ6PjHUHhh62kGTmgMX45GIeQnuEHkKBZpgkz9PLXuZzWr92zzaYlfIEhdPTcOE56JMj81RO78mVUrlGtH2ixxq26fskoWmMgco0Rt26GVkT4ZUL+BhgK37mZwNAO45nB42aEibpzjdMXCi15sVu0stTbRjT7mlY+lUsrR3J1CPvsZelq0XMTS9NtyfdyzmT+t217yxw6z+1t7L2cIRSUTaEE7CZBSiewN0VH+Byfial39f6dIi1TnKDBqVSigCbF3lrgnDmw8X4+59+wMvbT7kvR7aT8vh0C/It6/HI4SisatEb4IhF4sBWT7T9gb3ucHcHi/Gnh52b21t+/M3KP7/9hj//ssJj4aAnrzZItJ7GAYGXsr/BWqXqI8dPCn/373zAP/0b/wmvX/+U5cVHiB2AM+wr7dyop1+yPDe0Ne+YtwqsD0iBcvsZH97/KX/3xR326sif//lvoMKPP3vBTz/8mNc3dxxv7imHO9bDLbIUmrjXQmTv+7gdlIVbjkU5vnrk2B74QM58eLvws6c7fv2N8evHna1GO+pWcePi3RCer42UTynkrc9tyoqLPQVIxIvDbzX2VCiEallsKIv7pgGSP5NYnOx8GRSSAATp0bVukPi6STliItGfIJTshHwTpGZYQsQihNBQU1rEzqV5pk0RopUy4V3pkN//RelnSz2QcrlnjsVV+40S4rBbbP24gkVoig4EliBhWugSQsYuwetZJDthVjLlsojR1Njr7twLEcTc+1HbPlSkGVgUEe/8gAHQfawmeRtCzCS9Y1fz39dImT6bBe3vfn1PzoB05JIFMWz+eNbdhGDqljpDEee6zi8kypU6WYUhkLpycRSVZCeRWAvdAvfzia6oNMezHYFapKjgfjGAvnFiM2Rr447KfIBHG1Oh551Gm1zYY9Nm/vAS62UIXMtf+j3GAo+nFLNg0uawxTMGUe361fFe32Ax2RIAK+fpPRvBP0nPAfS8YIkNxXj24T2eFMicP53nzzP39TYTNaepzAv1h5DL9zp6H+DRcOXbQUw6eyAsYhdaNTait0RuoVz9PA2L5edFYbqLUg2Chd+QjtibFTKFcNamLpM8fu8eqBRMemENYZk14NdVKYjNgNJj3wlY+/TEvbUAIH5fDVOP4W5i7AzewCYJWNoEdGPP9PGf1s/sBZjXRYCvPsUYizbubnY+OcJHB6EUF9Stwi4bn6wLnx+P3C4vKfcvoLxgpbK0r7lZTqjdRcOgitgZKQdEDyz1QN1v2eszqg8UPXLQzzgcPuCj23s+ufkNH+mv+PPfPPDwLFhZXEiHd86tNuPQGm/XZ37ypx/wn/+tP+Nv/MGf8MHrn6DH20BVDdsdOu3nr2H7hmoH37cSILBWZDdkOWE3yv3hc/7m0riXI+W88+nxE16+/Ij1eKSUxSsa4mGjpRht3zB2rO5Irdhph+3EivHCDvzosHF8feaPbxtfPDT+fD3yz78Q/vWbB852Q5HF6yfMc0MC9FmYj+01lOkcFpzXZ8y5+MqXRMIVUKFFyqnPdZQE7rLNM1VUoz8EQpHiJbOt9TVrltbt4HS5ceH7ontbQ470nge5p2NfFjK0ZWQBCZM0nASpiqp3opQudgQvSmTdizZAbm4kC6Cl3XWfBkbcUVeR+Z6KS+6DOI/IwbpRIsTXAmQ0jBLemkKlmLFHWCH1okijthpdMH2fqTW0E5lTRofyb/4dn9vwKBp0MuO1p5rmemHmuHWdCcmnkQQvNsI+v+v1+4OBZFHNscYuwFOJcDk5/sVYzD4AnTVqFxHQ64uN91NwBxAQSZtMBiqLdLOOiSXEaQKO3h88fT6ZLpOvK5SVi9mYmLITSUPiOjYWvUkh+QyinqMsrZI9DLBKEekkNulT5BZ7iwU4WD8zwsshn4RGjrGlVZDu4VnhXnybS0XhAuMCXYhcfm7z2MSAXGj3PmhX9ypX158UkVzdWL4n4xwpQDL2mIDH+v+i3nqsB2tGFS8y7MTj9LxY2u8dKAigZZCk8piGObpPkNsRenArWta+99j9bhD2RB/uzHv3QjeV7s6TLg4HuMpp0tziDPCo4uxrAKLaHf6MewcFrY+D75OpjGt3OaSLGZeoAx0T5eE6gJBitNo4FPjxLfzRS+UPb4UP1sbhUDi1ym6w68ptMQ7HjePdgePNZ2zyiDz8Cj2fHGwvAlawNVNBPYa6LIVSDhxYKctCWY4cDht6fOC43HBrRw5PhZfyC/7d10+8eYK1Gq0YxZRilVYrD/cP/P1/8FP+q7/5n/HDF3/G3f1HyPGMLI3aBGxH7EQ7P1LPD9B2ZPF+z26VKq0W6ga6PmEcWMvCJ3efcfhEkNMjS3nFuh57S+O2n1AMXRakNdg2aBXqme38zOn5mb0+e4ihHvigCHflhv3mlqfjE58cn1ntyFZf8O/ebOzsZMroe/fPWDC+ShPEzR9J/3jI426IRIEku9zzeY4WQF9U0DZCczMsIdYiVR3Q9utc2hpkJtWFHPVaEH2Pp3WPX3t4FnTa2bHfxUgyuQYpsgaAUUrnXozE4Fm2CelRloshy4GLfRneJqWx0FhMWFVYJpAl4aXI7p7NvJ/IQpRKt0aRYCHE3zuNpsEqMlAzSqth+7oOq5J1FdJrkx6BnL/0bgQwSkDVJ71eAoCYg24hh15Q0in5/2vOQEsyig021bwQ0/VyoU/yuMY77ua5c9N8sm5xtvHPmCY2VKiAWCUrRqWlCBHrEjzdC4Z3oHlf+LyeddSlccLp0o4E3NWTYYW8ievMg+6icstD1ejumqwz0C3NS8JggqORyzt2+6U+fZ8CznPK9NM/6+Odzz6Bn7QIXC97zHkg0XzGoZTHDeWEfwc79QpMDMH0HmQ6P8/V732EhG5ltCZQRpw0N7gbBGHZpLIzqNXbBvcqkbF2DGKD0135Nexxz7Gu4dmRvtQtPAYQzW7w5jkdLkg6uixvui+reUr7yEytnAUilz7FmYetqkaYQL17XxOlqTfu2dru5MIeEFmmuUmAF2ty5vT07RZzKBFmEsVkQ9X48WL8/Xvhz24Ldy+N27VwWIvzLQKAqzQ+fvWKFx++QteF9VyR8zPtqVLlBMsZOx5Z2w2yupelSaWsB1SOGAvHwz3rcuS8nbwx7e3XiL3kr1vjxc0rPuXX/IV8ya8fHll3oeiObTfoDx/4p//ob/FP/vAf8cHrv856KJSyc26eAie2Y3ZC6rewP0I9OdO+hOs65tGqYlJZzxXKDivIunL/6iPqwx26VWQ/YXsoL6LFbKvsbcPOG21rnE4PnJ4fsOrWYDXQZWdZDizLDVIO3N2slPIN354aX56Ub06NX+05P32hTwo9V8YU9rxcQZe/pkHQrfw28EXPoTcIopt1Cx23IKlRqCnlkxAR+7HX+rmv9+vl+va3Qumb9f2ZXoNBvI0jY8+KCYuU5N+7BwKh4EBgFAMbnBjvaxExchtrOseuA4WZgJf3HvJYMRYxFjNKE+/VwMgQctJgg9q6CE0w0ggqRuzcak4epNUIZxlSI5SkLp987KdQnEWor99b8ujKEB2W3J153mM/5zyG7FEL40UaNPVaGO+Tv+95fY86A+l+4GpBxASEK4XLo6afKYlSiYWlItMxdgUOMrjakv8qUe2Kvnnc7bfHuZx9HTajb/wejlj8O8XACpZKsCOaaaQz9pqkna6b0kMQN9ADvzCTFb1D2KQJYnMP1358Nd6weNasI9+RQMdPs+sw33qXaDcLkQuhIZffjVNeva7GYAIW793xF/dh7zvhd7/mZ5k8AuPv+QL+H8+/d1GVT9fVafIfWoQFYk0ZIyTTqnf/BQ0PQJRDZc5UsF47It2LrTkAIDZZiyk3mQHFmPt0l/ocztZK8lKsewGACF9JtH3VCAtIpIV78dwq4taEuNu0TaGzzu24mPMUKi7sfYijHXemnglkpUVDkNp4ucAfHuDztfHqcOCjIhwOihUDLVE3Q7HV+PjDP+b25jPMzpSzsW3CXh88rVUWrBZs32g0tDSkrFg1moi3GFal6IFSFkSNXYwm3/BB+4iD7az1gNYztj3x9akg9cDp9df8r//h3+Wf/eF/zt2Hn6OH1UMrtlOqkT0R1M5Qn6A+e/MiPUJZadK8pxjpa2rs9cxaNyhKOyzIco9Wpe1fofUcbtwCWqjWqPVEa2+p5yfOzzvb+ZlWG9pWzKpnv8mRRRQtUBblpijcv+CvPQtfPJ34xUPhq28aZ7hc631/NDIz6rIcbSLMKebdLcpcj7mHghOfni5ccLqDqA2Dro9GKO/k6Zg3lbJJDvTiWrF2MtXOstxxys4MS4ayEtXYxr633c3vIbBs/+6A3+Wg9cJiqR6CWCzS93Nf5zMAd9RJZmz2gevhMEivmZjDkwUvjuRdHH2PVHy/OVhIyz+KNpH7xq9bs4BVym1rlLDEvWZDjZLQEt68eM5Yfz5IWYzI97TrU4+PXIrCwcXoINFybmJEOn9jyKs2g6Hf8voeYKAGism/oS9GERd8c4vHBAh+h+NLF26rdL3T59M5drnA4xoheNOaTZ052fhAG0UqEJ9YyWY74YvVqTjDHGeNDdPj+iHse5U2f6DAzCOLIGi4lyAmrFTflL5xndgTFQGnAk0XrrUkP3YXr0WFtxRcl7HEDgRy4pnO1RE+HQiMT+aFExCru63f506ahIEwuR3nc0whlIFnvws//O5XgqbwEnhPAsNru/u4JZdAJ/JMpv8kb3UO+3t0x+fbYqFlfnFn/+NBg064j2VhpBXu89PDFhmqiTl3kRhlXUNJd4A6l5LN/4ifv+azivNEDPcW5HWreJnb3Zw0WMUuFLux+3VsiWsoYiVkX3RflBZk16w7YdN0+e8v1fhYjU9YeSE3HHRnwRv/FJPYWcrd7T0fvPgMVaGdT9T9TAti3HJYKIfFx7meyd4FEmQ5E8+UkFAEixZuDy8orBS95dHeoO1bPttf0vaP+fbJsOev+bK84R//Z3/Ef/XTf8j9hz9hPRyc5VUruu0YJ4yNhrvubT8hVt07qAUtPlY114K4odBotLZBOyCtsqwH6nGlnY5szye0elfLSsPsjLVHtvMDp6cn9n2j6BG1hsiZRVd0UUSOmCglwiPtULll5eMXjT98u/NHr1b+3dOZXz1d7beL/WLD6983b66rBATf8d0OGozh0RRXMLjRJOFqHgW9JNb4RGLO9du9AknsZVTrJCqa9B4KTqKelRbN2f0WKYjFq3/59cK9bbGfKji5MK/Xvavxle6llcuHTj0UIYkcw9Qr6THTuOcCFBNWgaXRc3dMBIr3FRHbA0wb1IogtDqqivSgpRCcigzFpfy1AP8WQGuS+QZGcuBs6KOcZzUHbQizMZcSJOfH+ljFM7aQ/MGfqFYn4+G3v75fBcJeLyDQXCLPHPg+KaX/+Y5LKZ9I4qHTXRpurFk/vu8eugtKXDB1oRoT4OSRWOSSGQgltQEZhxk35MzwAQQCkacmUR3P3t+fHiLrKcQGc6CSfodJkWa5Taer01PgLgBTWgPuussCGfHJPBBj/PJWCMQr05DHhvQxnb02gbolFW7Ez2fr/mIOxvujJvd0YKLzd7S/vee9fuHpuaf3r14tinJI8XlIj0jed4Noox6gxywsjOkyYpQi7ObuOc2e8zZdMyqCZRZJ0FSo5so8Qw3VvDqghzJDeTdvm5wxUc8gEHqlMqZ/wrC3wppzMpTzD5xT4mu0Bj/AA2pKRdhaZZd0BXsYTYITY1px7sAZ0cpaBC3GshrrAZYC2/bEtlXqWWi70qqD3tY2WOBGVm7wngIrO1J3JF2W6ulxr24/5nC8o7GhTTi1E8Izh2WF4+KKvhm0ilWDcqCEMjB22v6MlgXKQtsqRQ4cy5GbG+WWyldNqfsDnz58xE9u4c233/L6zxb+m7/5z3jx4V9nPR5iPTREvMof+yNSqqd27WcHAoQsWhas7Uj3BuJzWisHW7Flo9UTazWW5YAcXrDfnmmP39DOj1ArTQU40c4b29MzZs+oungoHHxNiTqAX0CKYktxTwVH1qPw8nXl063xx9sNP/ui8Rs7dULdDBBVgwg7uZL7furgN+VZyhHpsoL4GEsLMuPOwzvp7vc6yY4EBr4xJBqJSSgpkWBqiU4hWQ03uvTaHKQFH8fS5Y/fpwksaMLR0INDHluXCROKD4Aei9B/sfx9GrgECjb2s2cjDeNoVaMoFPPsgdIcwi8h/3dtNCX2Vw25D2iNmncpgxxMDU588IRoU1gpDIQgE2Z/h54RYZ4aPFpG53wGmJvl4Vx11iwi0wnSQrancRpj2GXLe428d1/fI5tgJxEN3VXuDzQsnpTAucgUZInJDYZnF2ISOi0QsEyx9NRxgluiNqyZ9BLUWKBYOI49RywAgFtoXk54AVkZuy0GuTCex8Bj/ALRoCRd90glS3Wa+5ai1EHE2aUGijM/znwCWsSy1ASqYC0tynjABBFBMExmv1kdqLwPwuVMiPr5fPGHi4mxEKR7A5KoSWBhhjXbT2Z9H4166YZq1jYfLmjnqiULORVdKMVucea9T8BxeoB3wgop6L4jzJDxRvcGANY8dcnAtFB7pD31bgig5mtKSyjxjBGqBiL3c/mSsgBupZMMK3ghFAo0b1eba48KUgrWClJKYN+wPEqAFR8YP3dYZ5nWZeFZ8uMU2oKZsWu4SCmYLZ5GqMIuwm7uHdgI4amCsHu1QlsQO7McG5++vOGTO+Gju1s+PdzwWuGDO+XVUTmo8tB2HrYTb7Yzj814OjeeT5VvNzjaidsaHsBNaPsdIg3dDNEdRUGfeHH4mFXugJ1WBXn2NECTxoqittA28yY0eohx8AJErTVENpQKrSJS0GY0bVhRyvGOD1korWBPb/nwDXz4I+Wf/p1/xOev/oz1/obChmlzZ2VtsFWs1nDnbuh28vCOeBZEQfwedqNUoy7RD9EE2oZs0eCoNOq+wbJwWO5py8q2PyNtR9rKea/s2yPW1L0ApSBFETtiuiNH9T4J6iWOoVDZoO0sq3AvL/l4XfjR4S1/7SPl//kA3wBSK3UpUNMIqLHthVFDJfdqAoT4rBNUZciydClMoUSzy88bqfBxINoaRb19cI2YtuNo8+7tuIwTVajBqmds4Z7ZFT/U6ZZh+RPGfJbZFryhm6CqFBF/dMsU3UIVlzxF3CKvlHBqDB3g3tz09gYQ6Maqe8IajcVhtj+jCEuDYtXBAOntdb5X1eqNp4pQDLTtNGqfhkbzsFDorAa+dqWiuntoSI2DVhYxVBe21tisQRX2qlRbaDuwW8xL6rYc0yvZn6AwZaRkC+eRQmrB+Ro+4pS5ZZzvd7y+X6OiDoMm5JYLbUKY/fg0zYShQLpF/c7J80vT+4mar7VhfCsHMYFAfmgGvQ527edx3kNUIpQllD5kGpjhVpIFsQwh2UN5QY8hpoLsCD1dceP28hRAbDqF1ro3SFS8D3sfpzhWxnReP+8Yw2sEGWOQz9nH4XJOZov+8m5TaY9jWwCBPncQAkKuZiMFwDt3/c4xOYSX93n1utjU8e10ZVr+8y7sFlJolBS3/owJT1uwfKTRQcTkTojvTJgk3ev98g54Wz/jYCInSBKLGoZ96DWMJH8Wk8LwYxi9iFGYSv618AxEZz7Diww1CrSVTRrPRIMeQGzzcWmFcvOWH32w8Pc+fMk/+OieH9zd8WK946YckUVZbleWtXgvPdvY9426bw5A9krbG2czam3U87fYI+jTTmtnbMcb+ZgT5A43K3c3n1FYkbrR9hPSnj19SldWPQbGPiO2joZNkXtusddaa6jsnj4WPeybwaEu7GLc3h756NWHvProN/yDDz7nb3z2n6L3K4vgvCFrtLZj9TlKH4PVHatnat17OVoNS7FZi3UTdUVMvGBRwS22faOedigLWgq6HliPH1KPX9Cedtq20faNao1lLSgL3NxgN6ClsBSosrln4myUViguVLAiIAfWg3J/c+Tj241PD898dDS+ftoBzViR/x7pcxnvkiSc9r2THtnpX3JWCAUz3GLx+8Q1EOfLpKU71rn/PUtgV+gZ4pwIp1cgvktuka7UVCxAAtGx3OVULdp5BHmtghdvSy6Oi8XJK9evNcupwVnIv5KHJT1U0eI6rvTVNv87smvSa6K2YOLclb2sSFFKeGfEdoo1zzSK8d1M3ItQNtDGuhrH9czNeuaowqLqrcTUaE0xWzg35ak2aoXzJjzuxl6FVs09aCnHzaLFTRgNjDH19aBjvPvc1RD5IVtTt+i1Tv3u1/cEA1d/Z+GduMmxMGGwOCak0pWnXByeyHY2GMc5B+FprIF3Hy7ZlD1Vg3Dn9IEKhJR18kmvgYFFY5dkGjd95/y96UXXFNbBQf8990f+i78b1mNoSMZ58r5Tj8emkjz/vOnbGILc4/2VYzw2Zb/PiVXRb6wLhwk0cPVr3+DS335nxLv7O8eduM8JiV7H9cZovucte8/HCQP1MjzQwHQo3xZgVCdCiVsQsJsXNMnTSg0vQA/v+AcSa3PkSF8MSGxIBiYOoedMhjAbJEZtKv5iQfpxT1OSGSSOK269Nhd6tXkWQdYy2KywN6EJnAqcRcBucPdlQ5ry+n7jf/75wj/5g0/46cev+ODunvvlBctSUDkguqBlcUuugsnu1nmAx2oOmCqbO7nOj2ynJ57Pb7HTgefHtzw9nsEOmJy5e/2K2/UjtBjsFW21u5KLKgvqblFz61AD2DVTSmF4R1oLUmhj50Chcnp45nQ6cbgXVBdu7u/48ec/4v6zl7z44COW8CyogNZK256R7YRQPeunet4/dY+1oahEGLDFXmoSVShbeI8CKLLRNkHPZyg7TW/RwyvK+iH14YF62sCeOSyxFj7cuf38M/TmE24qbF+/4Zs3/x6T5sQ6zQwN6W5n08pxueWD22/5eC385G7hr54rJykhB7win12tP8vOffO+6nIiD55cwZKLdJIj3bLU/la26U5ZmdklmATg9Ws183nU4JZQxMe7DQ4BsT+QEYqQjO339zLckKGakTnk+2TaY8Q+MO0VPlMSje6M4SHtzz15K7B4Dh8L6QI59mXyaFJitp0mwl6Uc1koAosatEaxitopPB7CLp7qawqHw85yPHE8nLlbT9ytxmFpHIp3zywAdXEjsi2cz3Bq8LwVHjbhocHDpjye9mgEpW7ghKzz4cusoRjLeczNeij0UqnGeLertfFbXt8jtRA6ye4yKN1/vXzNSTHpBsl3U+DGYuiusDiJTQDA33jfBbiMmeOWmBEWWOJBJy35PSSXYWNoAIsBTgJhI8mSHTzMeaCp+JM0BySadhauP1vHvTbdIAzLP787eYbm6ez/SQUX43FZRtiPSQvUrcxrRZvfTfc+E6rnSqDMP98FRDnfvZDHxXeur/n+1zucg3eAXcyb9JHo74Fb/g2JJWW9lpTZuKcBAmPz9+JYMuBLjJWEaxLz+WvJPuzNQELo5OzIYGln2qI7Hyy5gnEPUSks1nuHTNI6K9pwLOkRjBTa7hloKNWEHbdAzizU7cZJsQhFlB98YPzDP7rnv/jR5/z41Wte3B+4Xe9Y5IjHLFes5I5r7lqP3OiswV4IiWW3HhqSI4fjDet2S33YWPUWs19yfmqc98bt+iE3xztUK9jGVnffEpIZOpUihizSvXVmGo1mnLuBKLVWbN/Z9wYHUCu07Ymn+zP1g1uW9gp24cPnF9zd3nJf7lmbUKlU27C2o9tGa+610L2xbztWN6hRiEkUKf58pRWfh9Zo9QwxBiYLVCemiglNd5ANPT45D8Re0LYbWn1CygFdK8tnrzj+9Kfo+gPK84acNr79+mc8fvVXHG7vORzvEF1Ji8DJeqtXdDw07m5e8uH9V/zwduVFqTzXJPuO/PHehTO7pc6M1r6KI+QqwxDpsmO23DsRLTlW0znV3EMZq6QEV6B7ACMUax1UXBoRiTu8O2G+55uylUILJV8k5iPqp/ROrCJUUVooPA3CKTgHY4/ruis/vQR5L+TmZzSky5tKUnAYDkHuqygjs8b9cJ4dUJ2jI4VdiocXmxduWs0Q2dnphdGRAodD5f7uxM3tE3e3Z14uwou1cVyNo+wUWVjlFrWV0pTzfmTfhaez8XgWHqvw9bnyZlk4qvJchfMOe03eUJ/ReM4J0L2TIZB/+1pI56pOVQ1/1+v7EQj7dWcEkEr7+oKRztI/M7oXYQ4X9PM7x9OaDYURRDBsIsZNz9xvB+sWXkdOjA0itPHetHH6vUecWfr4m1sS+XtY/RJIcihcu3zsSdHO9fIVZ4cjzikocUfW73V6Juu4+RLPpXCAUGA5/nlU1PmOJ7623Ad+aPRynX08E6hNYzzhhcvfJIc5njV5CpDK08/hDHpjBo/xBD3TZEqfmo7I0r4z4OixT8S9QNlOt/l1s05DDylU8FbC0otO+gd2Mcbdxuieh2Q2Tx8Sz2bq+cIEhyQHLXqaDwfmSLVq4C5LHddOPnK14BFIFj8t7OZ6s+EWyC7KJgvnJqAbwgLW+MGLM//LH7zmH336ET95fc/dzcqdHFh3w8pz58pqAhcEmnEM8IIGIAn3ebPG0na2GJkbXdnuFpZilPIHfCtv4fGBm+U1RQ5g7o63/eRV6kgrxddhUaWxuxXXFGPHSgkF3JzPY+4ebacnluXI24efczbl9u41Zm/ResvxeGAToe4VWRSpZ4p5OMBB+4618AjsO63uSGuUVkg2k2WmUzNaq36/smMCJTtkmmdaaDnD9ozI4sepIotg7Qm5Wzn84Ae8+OHfoi6vnKexNZ6/eMPDb/4NCqxilGVD9ExjdVCWwrkV1qIcF+HFceX1Wnm9CF/sG2YLkxAbW/MCFPeNdrklL/Sz8E4qccbyY726OIs9P9k0fQ9Mf6eRYgGWna8U3IGeGu7XkP4zV38CisxEEffepUFBrH3xfZWAJT0K6X27SNPuqbv5p4TXdtqsHRzE4KRnxYhsFidw1zCkhOCJqxffWpYkGTqgkOCfOXegoaWiB+P2duPV/Vte3lXujsrLQ+XlUTke4E4LN3LDIi8QK2iFp+3AqSnbSXk6G9/sxuH5zPEk3C3wsBlvnytPKKcMKWcqJNa9xpKphzE5F2bTEK1kWEinJfPbXv/ftTDO23jHv+9KyhfXpNJsNMfsSipdWNP7o8DEyLftn9p8pJeHmHBsuNSCLIdNA5XAJRC17f0enHOAu+lEHMVLYRAX85pZPCnPkf8sBGolnbwTXPDFZg4QPAblLu3upAqtN9viF/NnkTan2UdhnhP/Nxv7Y4Cku5pm7aoTsOpJKjlXEmzmyu98vTezYPrMlXN6f6YRyX37W7xXl10bc458ZE0iOtPHM2anJWCje4nyHkWls0dadevYAuApExs6yYqp6BP8yUqWpnbBFdUK42tdFJlDAekPaGFPKIQiT2Fs3Wu1RIlhidBxAckWxcJ5F1igtQV054PXT/ztH73k731+zx+8vOdFMRY70bbCc32irEZZ7jiUwnG5Y5UDi6xYVaR5O25ZoXKm1WdaO7NvG8/Pj7TWWPUFRRpFhXq4pciKtBvUHnlx8wlmexBjG9Qzyt4zDaxpVOA0nDeQhMhK3VeWJQnFwehphmw7237DVheW5wfk6QvkeGBvT6AfU7Tx9uELXtXXHA4G0tjb2QEB3ioWO/tz1DNLtQhRJHAPwNPcGpTi+yipI9Y8BxwVrJ6gKbK/wGyhrCvL8RVVv+L+00+5/fhPoBwRKvvTmdObt3z167/A+AZZX1LtgOoBK4LtK2ab56gER6iUM8ey8uKo3C/CB4dCeX6i2hqhjBgZteEat6t9kPIyuQGXlhFpDLjumDdZrmUhvZlpAWToKz1jcw22NEZVBGkaIHIQEDunQVye595XGmQnT4iUQRerCZKVJZQeoO5FGLZmVFHsD3aBzh00pDCbBS5J5Fa/WMRiFUVa7QkJDU9GdV6Li/yiBuIEUC8gF8WmpHn/GSqlVMrtiZsXJ17eNj64V+5vhZdH5eWNcntYeMkLDnZHkRsn+NbCVuF0Vp6XhcdD4+bcuF0XDoeNw1NlfTYWhEWFdjLOexpbLUBRejiio2NL8qOMlHwb8lMyhP2esPr7Xr83GHBiVWVmqvdJyIulNEx0a9BjV7lou6sDyI5Z4C6ZiEl5CNKtGZksc41VWVtzZCpD0Tt6DbrIZBUP0mKURrVwYSWIIVyodUqz0S0sibCiQ/h7684knkTaSV+FDko0nld7ueW4vwABXjM7OACRltNkJ+N0CXFc2fj3nSAX3fTeUcAZ+ph2g+VkJPghFo7f28iUUMRWEthke8+Z09B7lSd/ISw/y7ziXh0rCYc+rtmS9J01ggsBCSau9Xud1pqo11HHlblGmp5V59Ir6fguEdGp3tkuFoRqVDszXxme6bGP+4txM7ORwhyytUOQMK1bWC+ZnqTmCs9CWubZVH203M3pMfMia3yvReW+golSa8YtNdKwIsvZjMoBs7Xf624LTQuyO0HpB5+d+S//5DP+008/5JPjCz4ojVs7o+eFs33LevuS+9sPOd5+wGE5QisIKyqHwK6hHLWxlluQ1zHfj9zKDefTW87tjO2e/aNUDktBbs/I/iGH5RW2V6w+o1tjr80tavVMCpFKk+pCrYHV2CuyeNGfWlHN+Q0lXuFU36IfKvXbjf3xLevNK+RQED0jcgAxHp6/plphLTtm3htAK6gtnOuZas15C6mwioMtab4eaogCiXVB1HSQ5qlmxjO237DvisgTbVlhuaXcLby4+wl3H/0UXQ9UjP3xa+TNtzz9/NecT/9vtNxQRFiXFdUbTBUOG9tpQWwf+ntf0AVuRLjTAy/tLcVWh4bVy5eHOEnRMYH7ALoh+Lss7gTuWI2hPwZzP2W2pZsyThdguqhnZUQ9D1uEtsOKK+YqTu6rRL0PEbekiVbgaPc8Ztqti+Hi/Q1keNF6p1txGbKLN4grSwmPmYR3roAFETdDBB3ohLERXAwytNH1iitNJ4oWWoSXmu0svZN9xUvGw4Yhx+ZdCJuANFY7I/IchZU8vXdp/j27OXF/e+aDO+PVPbw4Nl7dCq9vlftVuV2P3C+3rHZH4Ra1BTbF2sK2GqdNuNvg27UhzzuFM0s9svCIsGPSqCacER5ao1UfB7UW4ZQl9GjqmJEt0jP6QvfNmuJ3vb6XZyAjrrM1e4FcExPIYHXGG7/ldxu/2/TTHIXNi8iNtxagOB1Pljp96EAiJnYxFLHIslhLbh7b41wBDnpBDM+vHsg6Y8sBaJqn3yRa62EN/PrplejgPi1PyxjV2KBi8/hycd9+myN74Xp6u+WblmjGFoPMN7p9hVy5QokOatp0Lj+fdqV5OY6qEXSZvuO4wIbO75cYgujyPhMfDmBzeV8RFgqQ1/qzX8KGzK9tzUlwqhpuQ/fwZCjFfwSJMNalSQsSWyDxnJfcTBluCEEreD0B6XHRHObZUpnARHye2F5C2ac3yBBMC82cRV8NMKWpsKs/8xlPKTzvhXU1fvDhxv/2r/+Yf/zZZ3x2K4hu6N6QeqTxxPH2Y+4/+gPub1+7JVrx9KXzE3V79GwrrPviqoq3hitKsSPrzaeUm49Y24m6PXB6/JbnttE4gRrHm1uOhxDwtWHbCa3PUJ8QE8rqDXiWJHxSqc18jZWCbMHRKN6+oJn35NgBuxM+/PQDvnz6NW0X5PmGw4vFuQnsaDmgVjg9N6rsHBf1VrJSOTdl33bWAKFuyLll1MJTNMhYzhvAPFThYmQZ+6DtsG3suntWMZXy4UtuX33gILVu2H6Cx695/PIrHp7/J9Z1QZcDshSWY4GlIItSDOrekBpEuB6m8Hs8qHGjUFrjHIraerpcvKKrX1+TYcX3ZmhdVtLx/7saIPbr/JkqotEh0JL5Yt2K78BYJvJzhNAkIgTpnrdc/CHjLEOiE4cGIgtHfU/ZdOyIWibJe3HZ22Ob8zXGe/1G47MRujT38gBOUox92sQ9FYBGVlkFdjWaHRA85VBr5Jk1r0Uh1iiyoWVHdUOPz9wd4bAK62rc3Qn3d8rt0bg7NF4cD9zowgFlEe/9QFXqtrDvwroI6wK6GKKForunOeqRSqHKTqmVb5vwvJtzYC6ETu1zmlwmD9tklkcjM+wswpK/z+v3BgMDcQwGpku6CQyEB+C9bW8vLMBr/7BMP/J8nbIVn2UcysgUu1zArktnxmleY1owMZC95W9XcpHaM6DtuC2tQIE6WK/YIGWImTcjsh1J12nLCfJFRPdsjGeaVf1M+ZOrcTHrA/dOGHAcM3+gft18BJlH0C6frQuRnE93jyelordTz/uUUfq270foFjMY3VK+skjoy1GmZ03UkO/keSR6SwwQMtjUodAJt2bEjKy592Kw5OnxTkkQxzQU/b79Hmqs20y7Hu2d0wNCb7qSxD8lCWgOMNP92olRhoMHgUw9zRWQmRDVvPthzkAzoZriNq9wkiUKDD3ywavCP/nTj/lf/OhzPru54VbO2OmEbA1ZnpHlFbeHz7k5vkSlIrVhW0NqQ6sh+07dqrcP3olaCSt6s8JaaPKErQdEj2i5R/SG0u6BL3l6/gbbn70pi+xoabQq1OprSkxD1xQHOCIUC2VQd3SvSNvZKV6tUH2hZcivSYX7W6woh/Ue28/s21es55fYuqNyi1uGhpSVvSpyNlZVZHGOgDalIJ6iVQ1JCGbRHMbCy2cSnLvmCKzoWOfNkNqQulO3nVVBX8Dy8gMoC+xPnra4f83piy95evNXiBTWw0t0LcgKerMihwyVCGsVavUYte0upBVlUWUpDgZWqzyKduA/sZ6u9gcdEMwvX4eXBkTvQnr9SlkUJd7NvDufO7piXmK/5VqXfl1X3FkYvMt0mf9FSp8ET2YWFoT/NI5DckqikFfwZ6SjiGHti8okvmSIrpT5qfS7uEtwknIp0nXN1wri7eebNTaAUtjbgrYTpVasrRFCamwtgKNUZN1Zo4jXYTFuDnA8CuvaOKzG7VG5WRYOohwprFpQK0hZvMFYKRxKYRVz4uux4DygSlM4WaO2Rj3A2mA1sN2Lj2WhtRHjzVCPDRAgBg3UHJDP/unf9foenoGFbudceAJsKPz5NVbRe9Zk2ib9JLGC86H8wYQWaymubS7sRQWaF/vpl3Mpnica99BXh/RfrX8hxHCmhfXjtT9XFtwQJNJYQilaIMYEJAZYlkT2cEEnAMe1upehXytXs7/hd51V8OZ7N2bL+bpPATB5LSbm+0W2xUTC7IWIpmftSr55rLVbxOkky5suI83yHYWex+f7491pQfQ5SJqdxP2ZeVEgco47xsxFP8oz5/3m+37cuJc8l8V5pQultKxCiCZAMy9QJAGqvVtdergk0tajV7vO5VbHSI57jeeveOxXPT3L64R7Sl0SFbMlsetnoaIQKVu7QG0bn90Y//VP7/mvf/Qj/vDFRxxeLMjD19gTiB2wtrGur5DlTD19SalCaVEYxcCqrwVVwsVf2Z92b+b0eKAcVspxZV92lrKhhyO2LNSycljvqbvSTl/R9gfYGmXfkfZEtVOshcWtzIi3NlFUPZ3RmrLXnVa9lfHeNh8TDI2+DNUOlLt7lrWhdyvt4RFdCtQjzc6gDV2I+HNDl5VaDygLWr9Bm6HrgbZvKOeIXkkHjIZ5873mhEX2GtShRtuKkxmTX1Ab2IbpA/utcHv/kbdg3h9p54rZG7Zvv+Xp619h7SvKunoyVBHvaFhWN5AiVLWsQj1B2w3bM5zkFQBXNdbiPTMUV8JqCS6hyyfNHfOeV8jNzou2lEXvARG5VyIk4Of1uHPpngPf28kDSEArTTvgxcxDrhp1PCT2q2ZW/7xHYKQY5sMnGIhwnk7FiJiUftoUql2090y0a4NztpZEInvlerAmMq0pFqHvJm4abk2gKYfmBa1u2ZC2sbedwo7qjhTn0hSFdTFKAVVjKbAugqpRys4ixoqyykKxFZWV9eAhtVZWDuK8pVUbWlYUYbEFO1f2pfGwwroZtx4toTYb1f4zw6TP/wCISnVuCtKzSN5JPPiO1/cAA6lcQsJ0i3+P+Qg82zkBkR1g43iRycrrSn9o6G4jT2CjG9X5hkgIcu2u6dEnYEasdHSZVZikX+cKK7X8TyNLSvq1FbOo8Keh6NQrUKFudbVWvaVpoEhiUzvRw4Lz0OKzfFq5etYJDMzYqQOAGfR8h1CY3+/s2yDc9QpXeQNDifVYZr9OABeJTRybR+Lz2esz8Em4GEmmb8x/j+PFGXKqLTd9gpwcDe0JJBlySQGUgk76o8b7GYPs9+c/RTx8MMKI1p8zuRvZVU1DcbQaQi5Sd7KCmoiSrbKs30A8p+BehvRcGT1+N7wELjSbJMT18FOl0UzZzTuz7YAXzW3+04yjnvk7n77gP3/9x/z45hNe3N6grbArLGVhO8N+XDkeKgvfoE+PcF45bWcM5XA44sx4vxddKsZOaTucXUlvj0LbX6BLoeqJpk/ozR0sq5c0Pt6j+8a3z2/Zt0o7nWE7I9vOXs80O7llfq5elS+XgWrwN/DQjZ6CdFmiPIdR285+uEPxDqHr7R2nb986ia/tOIl3h/qM2eJgtUBbbmh6i20Nrc9oeYR9w2rFurfF15+a7wFFsbpTt60DVomKk90IsYbVjVPbefHBa/T+BdWekf0tJpXt2wfefvFr9v0LynKg2UJZmhcqkhXB2/1K8xRRLYVyEPY97yGuZVCisFCjIaZR/TINBAOZ1m9uDBlc/dyxubU6k/wCCAxvleWe71b8JAPiRDkunmYrzmFoHp92QO0Q3rH6kFme2SxdaQO9G+cAAiEHpuJCfR8NokF8x8Z9pji4tCfG6z1u0/QM9qEKg0yD4B1iDtGCWvVsc87UZtRzQcqGWqXsO9gZtY1SziiN1eJJxEubu4Hh46xqiD6zlBsWcWNRm1FaY+Hge5+VskLBq1XKtrOeHRzuq/BQ4fX+RCvGyekGnFr09CD1l41n64K9gXkHyuS7+Ty+Z8ze8/oeYICYmFTUeYW0yvL9RCwlhOJAk5YK6WJGY4ZFJn9GsCT75A9wMDo5xWDMqy/9jja9N7RByPB8hrhmLgohXG2JuhJYJLoSUteZeOqJmEENpdOFif8cm3JkOuQmnUYxL8xQrmmR+jFOGMxhezfGPoODJBSlIvPXLGD68A7lqqFw5/QdQMRBUYiTfi253njBt5B8Xpm/If1Zx32PAkK5mhMKzFyTC2u+C7MYzx6f1AA6KfBc4GZqop9HSXDQ+1fMawpID7Kq9us7uSxipCZOskoA0kNWgpfUDaAR8VxrgbHErYYo/uqiKJ9dLgGWmc/UGXcJbjTMdl4dlD+9fcEPbj7jxeEOpUJplHaH1GdO68b9Z38DqU/Ic4vtt1PWQmvGeTuBbahE4SHxZ1luC7pA3Yy6G7V+G+WVD6iuLkEOB+R4gNI43N+jj0fq9hY7Kxsb9e6G5e5jTCqtVba6B3P+EMWHDKyGonJArDh5UMznzlqj3t9Q9ICaUtaXCL9B646s1e/FgLajcvAYrBlFdqRA05dUWeDxa2R/plWwTqqV2JJBTAsegUcpMpYa+8OC0BgK6HhfuHnxGmyB89fYtlHPb9ne/Iq6/RyTR5DXLCoUXTwsIBoGd9c0lCIsh8L55OskCdEK6KnSNs9yIAyOZilzmEBKX6lDlkQsf8jh5MaE7Mlsmgk4kFups//zjaEwJPZh9kfwnhthmce9l77WtfMdRYxRodALZUmCF53ku0WdEAkZZkIIVbxtb5AFuzEDqTaGzJyGZFgHXc9A2gdDSYp6uKhUT+2uQVQoKMXUs6dsp6ir62o7WzPKnsaU1yYoEuai4SWvJ1UiHcA4CNT436KFYorsK01WqEs8WQVdvL7FceehGSs79winKmwbPCnsm/Fk3RzBjTXtU5/ZdjkMw7MS83GRUfLdr98fDHQmSNAxu6IcLnVHJtf6WMbnM2ClXG7ACx0zTW6yRfPttPC6y2C0x7yMI+WxRhafoCPbtPDipH0N1Xi2EvfXhq62fKiIN2U5DD+lewsMuu0XiL9ZDevDB6S1PG5a3iYxabGBpYVySGU3TYNoV8A569l2tKX3QSyeBf9+INkuFsyt+BQeRJ+DCbe5EpSIlVuEDWwJRWhxzzEPcyWv7vHJOeiNPyfQI6SlYSZ9bFJp078hMb8xDheFqXJj4GTADBX4COTZLsbuAojGHOQ6dVa/Z2u0uC8VCcs2PSQpLPNajUWjZg+MRkgyWq22JniHvHF/TX3dJhRq4vXgN8JStUJdoNjOq2Xls7t77l4cWJYjpTxz3p+h7TQxXr78A17efczpzb/HHjZsuaGtu7utxcuqmhmtbhRZY12H8C2e/iVi7OczdTNs21mXBZYbWj2h9YZlucVYePnhB5R15XzcKHdKWV6heodwwGpjkcq2n5x5bsK+bdjTI1K+RR4e0PrMxpl1Xzmrj0tVQcsNgtLahi3HiP1X7FyRw+gbr7pjLqpZRGGHtixwc0c7f4zZF+jmjyjFlb9qEEzNOulNYj8MEt4ZMa9WuZpwXs7cfvQ5Ul7Szk+wPWLnyunbb9jOvwA7oUVYyoItoHpERL38sEa8W4qvnaLYobAsQit+Pd3Ato1t37DdsN1oB0OrOHCcREMHBN2gSjkQyrXL3slKl44n+p4bseZc+grhFWnq692b9qRXNEB3M+96Z977oUSDqAwP9DCcDgWfWWE1QgEtwbmm4jKcIFgYuypSEjsHKYWyDnE5Nj7XW9s/sus3XGubXXrVSa0RBPHmYyh1p2xG0cphi33Rzl7gquDAT4TdWvcj7+JEXCtGK4DeUcprRF6gckexOxY7UlhpHDjYgrBwFmhLYy3G3laKnVip3FjlnsZzhcf6yN2D8HxYWGqj7WAdXE9zP3lvnTvlOqAkt43C7/P6fp6BfHU3Uyhxg14ZS6Y5kXnGEizkh6nUEtHmR9bXAqRyybh2G4gyFrhMLq9LPBFAoH9/vG/XC6vfX56h9Vu8KHeJL/TQvIi0YN1P4QGLlMMWaTrWutCZHe7zOHWPXX+AsGan+0ol7jHpsJGzL3a3tOPYtAziXJ5RMLgI8/yMpkFzqhIUyXziyfLuyDst72AnBJjJOHjnN0Sc/aKYU3oIejnh6zUynsc6iLFcWhevbt1YF3fDY9LHza7OPn2fFhslwh/5zAKiwdCN66gJaBIJMzsj1XlAlrCgktfg2FUjJdblchO/ago+Cwt6xxsR5ZgscV/HBV6+gPubFbtxIVz2hu0VObzi8PITRFqEqgxtFdrZQdjiCgkE04W67+76ZPHnA/bmqX9FFnbb0F2wbWfTNxx08RDCnXH45Mjdy8+5WV4E4PaGWt4B8hxOM+WgR6wKrVaaFnhxQz3cs988wpuv4PGJ5/rg+L4W5HhkWZxDJEDRA7UsDKKvp7IiQmsniq6IVkQOABRvOkG5v2c/f057/kXEStvwVuUqurKytReQ8rkqCNiZw/2R491H1LbTzm+x05nz6ZHT6Ytw5R8oRdF18VBIVs2TAJCRkSRhHBRdYCmc9UxtDcyzSAwHi77WPdNiTt4Z3rP3Lt6LA8c6N65O0L8ragESJoXaZVDK7iHXRxfDAMsJiOPYZtkhtMUzj32YbvNOkojx6UaP+NhbYhqEXtygI5q8wXdlxMWv1xhgWDRDPjRfA82agxMb8nnJoTJDmnO7pG6YPdFkA2tYSQ+e9BonLUJhFAdDi95yWD9kWV6xlJes+pKVW0pd0bZQuCGNSdHinrpmlLZxsGduUO5QnhFuNnhRd562jWdzMPvQjNM5apGkbJueXZHoyZFzmQR75fd5fQ/PwDzoOco5G1dkwIvPpsn7DibDZXOZRJsJE9KFlJPbncokooRZ5KfyT3RgDO9DLMjQxO9l3CaDDJvuNxd0AAwhhFT8rQ1tex98wtWnWI+NXtyjXI2FWlcgw3ncD7wICaQbLseij59cjq1djXdmX2RWyJCL0cVPIVNSeiOSUEwajTGyNOoIFSRgic9l3P8YzxnjxJzJpMQDkHRLJM83uUpb/30aC65e0xjMZEu5OPJSkAw1wBDIWjqDOZ0cyZ1wvrqGN8NIjw60qEHhg5B1ISwQuvdo9/HJNKoWbmrnCbgQNhEqKwYsFXZRFm0sS0nnDW3fkHPDTFlef8T66mOKCstyy66/wWgUKaPkbsz5snjtAmsW9f0DcBYihc4outOsYrbSDE4myKfG6x98QLm981oF+wmVzUFMi46euwPeFueurWHbHvwgF37L8Ui9/5Smz+xv/wP69ISwY4dDZN20IIop5XCD1ee+J7QQwDu7QLauNKTtLtgPgr78IXV7C9++DQAe9fNNhrDPZRDgL3mqrXkjmqpnbl58DHLA6rO3MD7tnE+/AXtwK74uLMcDsgiqK1kRVGRBZMmN4CTGEPpygLqBbUGYU+fHbK26UWMZWhyA+0Kx+6qJfZ9cmVyg09LOde0GPCOZNXbr3EHtao+4ehkSyBW//wsmVewDCzncfP0g7o3Bw7seUhW697CjEfr7TfRCa7jnbzQlSuPjkqw8huEd9WIXJ4vKivG9GC/tFoWQ1WS9V4nR8MqUu1WaVkrdKXYG3RxEqUT1UesRjxrhEA+P3KDlA7S8QstLtLxA9A44AivognHEtIAd/L3mLbxlPXuIjEqpT6wb3N3A6Qw364m7qrS1Oqn3XKht73VO+hqYOBcaA5RhVePM7/P6fgTCvHhXcNCTSdMv1a29UG0iYS1fi+8ZUNjF5+/Ew2eUeAF6pZ9KwCtECYz63fTJHx6CzNmM9+bP++Dm84pLSpTeLyHZ+lJpGjHA5tWpRN3l5LWvfWu1yGfuyqqv6ZmWV/uIpLdj9gvM45Lx5WsiYXoMLp55/m6OeFeU0/jbDKYulWSfB2EAsv798WuKkw5KrgH99d3Mc3T1ymdTuZzzd47PKe7rcjD5B7/A3vvdnm0QSqyU0sffLRgNq9e/2sIbJWk5iSBSO2N99DhwEOA1l7LpjLlQU/zzGuMd7OvWkqw1wFJpsKtQt536sFIfF1gqjbMr2mPh5uVnyOGI1SeW5QaWlVq934DzWc5oyZr8O0spNC1uuTfBpIAWymrAkyuPVZG9oLLQPrrj5Y8/Zjm+xOwZsydEnPjouLi6cjxV1LxqpVCRdqLI6sVeNidluUA+wnFlXT5jb1/Q3nxFvSeEVwuAIchyxE6PDhAWB6oesvEeAp5JtNOseIU4W9zVfV/Q7TPq8ynApXvqfAV4KeTWFaxF2mHFeSXVu8fdFPTmpe/j7Ux9OlOfv2ZvX4aLGJAF0QXUvIV1axR1a89TCxRKgfVIW1Z0L+ihIZtSzKhioXjg1OL+2gDbXaAlcIbYL7Pwm8KHF3tt8hLM8izl4qSQO3ju3sbk1oyg4gUXIJG9OfdCpNunHTA4nnFAoPFdcH5B6i001mTKQJHgkIy91eVI90ZOj/G+557EkfQx6n+ANXrdggBq3fPRvQg6+ABxbffjWZfn6c3w21b/qc6TUb1Fyg3oHU2OVFZEVlgOCEcoR8wWmh1o7YDVKLRk32LF+yEsCyxLZSkb67JxXHYOWliBgwqLwqnuCGtfx325pFFtU2Mj+D2DBN83tbDHhmflPauRgcSu1NjlcV0+y8V7Hclxfe5rhDx/b3ymadRfXsT/Cgvu3e/LKLSRkv/iHMnEr5MiUEx2arjdvFe7ZxiYVecJhILNVDOJa3n4eX6eBE2xjeeVnuOYYYYrwHThMQAM7cKjez9iTL0ohaP5SI2gcz5Exp4zMFp49xJ8tI4bkow4KjtOY909LXY5/e+8Zu/Oe14SgmYCg+k07ofE+zm+fjfvW5Pf/WptlLsezzOBtPRWiFv7TaXjOWFgO7EYW2GA1jan9Ph7llUgSVKhdieJmhPjWjx3FU+jfXoWvvrFRr17ZmsLsp4wO3P78hPWwytEC23zFsCROwjAelhpVqHtlLK6J6JumKyorjRdvBKieeijCeiyYjyzFuW8C8eXB4reYHKmmiFbobQzygat+tqvxYWNbVCfvFfBDmey8qHQ6sZ5O1OWW1RvabbC6w/Ynx8oVlh1HZ6oBiYLYorsFTvviB7Q4h7CrCdhFv0XUJADoOhyxm4/ot29gcc3lFax6BvhiMLXr3c5DCVlmlFpNirr3cfoeo9tJ06P31Kfnjjvv3TLfq+YFpYSXBKZ99iKSAFZoCzI4jwRM99fLAU5LN4xEWW3xm7GuVWvc5FAICqXdl5K35STcpN5nae8sr7/Bh6fgPTFfs1l6Z6jLmawDsB92UfKYqTeWchoIb3/Ub9BhKZtAgJEmMDlTOuZOW5NZ+bZCCkO44ecq+nXOVR7YQRdcMnyF670SrjK1UGYmrAXvG/JAp0cmA6T5mRS2T18JuFJMatRa84iG6JfAC/B7KGiZuprQBaaLpg4V6CyIrrSbMXsQNsWqsCujaRqt9A1i8AKrNoo4o3NlcUrn0YVYGujqXraaE52hjlFTInui7/H63uAgUylalcoNS+eCuMSCPTFLIKXH03R2EXqlCPL5ULv4KNO15pUyYR8vbQvF0g1ByQHKqRu3G8ZUp18Bt+o3pdgfu5AluQ5lQEsEjn7/bq14e5JL2UZSiNihL2ojzmpUIWejal4AZx8vnQfzq8ZAMwb40K5mv8+LIRkvSYginuPw1XEN1wov17couv6iGf293wMUjD0y4YHQS7uMwc5rR4dngaynOk4Ph8g53Z4/yOjIwmRkgs/N6aRbskhFMbNXVdezL8HIzfGIsl1mIOSAFgWa6njnerpVYt6tTJr0ZRIZkfrBOKC/S2YEx6jJkVjZDB4aVUvglW1oqY8Pil/+e2v+eLNG3704cfocs/GW5bjHWW5jXrqR6wEEGzVQVFZWLRE5q8zmmvdMDvRy5RGVTYn/wltLUi75aQrz/eND28UtgeEW3Q7Y/sTbVuptaHq19naY/R6FxZRmh6pS6VtO7adWCWCK9aoz29phw216FHwwQ/AnrykdDUWVU8DLCuKcn78FrijrEdaLZSl+NYUt+qVJ6wdacszhVuKLcjNgf3lp7TnByck6opQaewRMth7WKJnj1QvBWxqLC8+wIi6Cs8n9volsjwh64GGcDgKWgQTpdgCpmgxRFYMb7oj7FA3rEYDonKklCPLunI+G5tVTrVy2nae6+4k01iMQ3dfCfAOBqb3+1qXIYfz19nDmccwAPNskCSnpalBs+CT+BhnCTWvZOf1GNp37HtE4vO4T7UOAvxKJYySlP8hFSaLFpEBOrp3IMdgHo/8O59t/Hxn7BSvbaDGphJu/4ZoBuk83XBlo2xn1tZYZYP9jGmN9OKgNATQsQwPq6Fy8JoZXpN7VBoVoak3WrLMt23eH4Tm4HzfT+w8UNtb9vaA2QMqZwjArU1RbX6ftqBYyKhUbBagLuch5zlK2CWB8vd4fb8Wxj37bLIsLWcrZvxC6IZm6C52DeWWk9e6su6vnOBuZTEt+vw5UtAyraKT9C4ARTi/4vte9yDusQOXdE9NyHmyNscNzpswFGqvHWDdsncFEgPWdLr1AVbArTG/76E4vMtjAJI4h8UzJlkwgOl4n2kRmA09L+7e8htok1IdcyPT88KwkC8cEkZH6r2WRMxJZ/lPczadtYOK7lHp8xcKU94VetkwKXP1c64yRurfm5Vs7IcpJXGcZ4CTUcAgId8Yu14PQLSLlEvjwgWkip/HP4/121KwxKCEIMOI9KqM7/py9g6K1WONmeZqDgQUtzxEPOVJzHhbG//qzTf8xZtf8MHjPa8ON1g50JYDHDwjx7SCHjC5AU7QKtUE1QVdFapbaaKKtp3atshwcQtGTajV77sdFuSTF3zw8gVsnl/d3p7hvCPnZ6reUJsi9cTt7eqWujXqXilNaHsDVW7WhZ1K3U54vr1SGtTzjhWD84lSCrQjnB9oN7deE1+MshTQhYqxNusASsMz08y5BRZdC33bHFwxq7Dc3LEdX9CefoPYGnswav977iGGx4BLMypnzvXM8f415XjnzZv2HWmPYG9RLUgxiqxRcNUQXVA50KyGi3gNTkjzKnexfvdtpxyMsh6RZQXZ2dqJ8y6ca+Vcq7PurWDixGMig6Vb9r1oT+t7rK+z2N9ZwMc3Q4CCjMR1GZ37MkKf1EHg0xLluRtFs6sk1O5pzDCkYdSo6BhyT90Fb71fjHsCape9OslTGRW8I6zW0riQcZdeVfFCXF0CnJQ5iUPCS9BifyZfBzUvVREVIm3do0jU4hZ+5BVYfUYrHGmUemYx73Xhssi6DWkmwY2Jgku4vKp2pkZIqunuVUY1gUOEuVhotgY/pVLaiaU9stcHzN5g7RuMJxrP1PZIbV6L1E9qeIfO4T3OsEdmFoR4CvN1VGP9Lq7e9ev7ZROEhTO0RQhmE0bKYcInxnEDxoXQc3fHhYK9cJ37r90iRMiI1BDWTvsgFUK+T1q20/KPe/FQm0yx2VykMh89bqdrkulsAtGCsLPDoylmR8wWHcekjdDC6GQ3mObEE0luTBhjx0QUjIOHMraBBOPVQVZXsLPyTYs5PRpJMJEezpjvyRf4PAmzoh1pc9e4z9MQQ1AZnYh3SVjy31ukfV0Z7EC4rVuOvw0XMpHCNC3uXnlxupFu9SdAG5+E0W8XIYIEHMET7HJHJLuojedP8Jkk6Zyg9BzkMbkWPGsiWuSqZBaTh5hwN2tDnZncJAC3ecU3aZyL8S8fKv/9L/8dx0Phb5//gPuPIzbZgghVw9NVDq5ILIiC1dw7kHXa1S0SoyGtei55gpWmbNuB8uolNx+8dhG5n73nw7bx/NU3tOcNbjbW9YA9n3k6b9y8OHJYDlTdaPuJosJ5e6Y2d6WXRdm2vQNx27zGgCxOBiyycn76FrlZ0Zsju4ZlWpTb+1e9xHda8x4GKS5oq6HsXi78/IQdjiAr61Lg9iNO29dQN2rzsIDXOUhJGsrNhNo8tXC5u0fkBjl9w/Z0Yt++Qjg7qJKlt/CtFRY94HFbzw6ozemgIivoipQFZaVRET1iy0oROIg3IHs+7zzUE0/7GbGFGi55KL16qUUZ65qlJJvvUltiPzSIXrvusJ1Dtyk30kVsMPhO4CEJlzl7yAcTV9RL3wAJRsJQMc+gMnFpp9NnLda8snTsUTO8p4UM8SRRdsg5vdAX3YObGzjOdbHHu0z2H71rYooMPN/XZEcWgYM6cD4Ist56I0NpDr60ItU9sHpqrE3Q1jyLIGs2NB8Lr73knrQEBR462KleGcTHTAumC+jRy3uzsMiK7ME5YkfsRG0PlPoWrd8i9Wtsf4Ptz9T9iVrPVNuocqbZirC6rG5p5IRksxb6MPkwQeA076oq6P8fig6lj6QHpARv5BNI9FL90qv/tel98e953CWl6Xf0yu0LIr+vU2x/Ul0JhnMDEAS7yFtNFz3dVQNRYaaj1Y5CA3VbHmN5/caAs7lJXHuYGrXkxlBPQQnF0MyJNoq3jXXjNBRyumplPEvqNQl0PWOjruAI1VPEyzRcjLpdHAvhUQgCEBYelWkzNabQgDmC9YWVLga7Urbju5cFkAZaHyEJ6XOex18o6ve5r8xi7UQdgkQWHejk87Z+OFen6QIlAc0FmHnPmMZwOUAYuV0Bo7AQZJcloXHFGmDFasRZOxhNiOnAo4UwFxGaBtCP0EILIpa7Gf2cTZq/L8pbafwfv/qSL+tbfv7ml/yD5z/jb/zRS2fxq8G2eSEePbCpuuV/8vQ+O4in67WoP28lFKCxV+/kptWQeo+sH3Fzd0upCdYL9ekZnjZs21Hbafsj+/ZAoVDakcdv3nJzd0MpyqIHsJ21LF6C+HymqHqRHxqVGuEdjZYgJaykBXt6wl4dfWtESqSK0HajpNxvu4uacvCKulSXS/tO254dRJaCIpTDLaK31O1rn6dWY9sFT8AMrTubNmT3OG25PVLrI/XtW7anr4AndG2eMeChZGhev0Hl6OuCJXbRhhQPZdTlALrSpHi74/WlN6VpjaorjZVt36inxoMdvO5A2T2bojRaySwkHDjVSF8ssYdr2OgaxtAeRL2+1MMsUvF57AZcDGSUpZMZ9Ad3ILLdPGXa0mSoZAvfbvao81syzh5MAiy5U5lu2Y2aYVSIXnEEZs9hqpduAHCpd5jes/fJJmIsnGTKYQkgsKLHI1ZusCXktGxgz4icMFZ0P8FiaDX2Pct5K7SI5nvPLPeihYXeyFCmUsrqDbWWW4oeWcqBVRdW87BXEXNPklVqfWTZ37Lt3yDbl+j2JaU+YNuJtp3BNownqu2YecdDG6Mc3gof/56ILh6ulAjN+YjXd0Kk3/X6Xl0LBxoz0q3jdzJlE4zZYhDVAjDMfqArW3Qs4nE9u3rPoqXl+J4PyvytXHKD+xmx5YjZdpcbA4WaEMVZIkLW71OHIkLIDdMBRLqhRbwOUSOsYXUQEP1x5Yq32AMGkqkqcrUxIZVhR/nTuDSMEktgMOKj7/blhfoo22+Bh5nXL31Mk1Q0EHcPH8QQ5nv9OpagQubL082Ed66pJJq5vOuM+Y3xvRQEV6DT3n3m8bHF/wcISbA1z59/Tby2Qj+BXhw/P296F5Sw+BtOVJPIICB4JCEoW5BCci6qeT+CDBsk4ar7aCTD+W5FaVO+1JX//uudh/pzzsszP21/Gy0fd3cuDYotVIp7C0w4n07U3TgsDmYFoUWVNwkLlbo7IW/ZKK/vkSNI3Tr4kWXh9PzMUvzuS5TjXgSUjZvDgf18djdswRnxbfeOgmbUbevAV7CoL+Bu5Uz/ElU4PVHtVTy/Z3a0LMpTK4UVVS/ORBbxiniZT0fzEADnAF4rzRb2faMg7Fs0Ekv6kQg70ZL37YnzqxU5vGKv33I+v0V4A7pFXqOvJbXVJYougNDM482IhCJfoBRUi/dW0ILI0sOruhTkoGz6yH74mreffsHL18KL48L9XeN4uKWY8bwLz5vx8GS8fYSHR+H0bOxbQ+qQb+5x1CmRK/ZeN2Dy90QWsdaNft8uABvOd4o/LaozSjRbozovBg+BmMyt7EP5qrgXQj2kgzRMYpz69rwEBRevq239vkPGmyl3Yq9EifvkPpCFpQ6F3r2QBdMjVgos6hwbFdQ2VsWbXy2VtXm1wsLueyY8bJBgwIZhKC5/tRjLsrAsB4quFHGOjop7WRYxlqx82RpqO9QztT2j9dHDUe0RsSdoG2ZZh7R2Y8S9sTVkjM+14fgwDeyh7yIN9/2D+J2v/4iiQ0MBXEwKOv3tMbAOBtKdf6El8pfhWr7QCldWWyrKtAQzrWUypft3ewKhucL13NdJsSQEnv7229PpfP1CfQH74I76At2LrxHL1vSGiJOKWtz45MLrbjoZbrPxSlVyOdozqE+vRAYbhnWet5/kjvl4Juw1AykuLfUEbnlYV5YJCOa5Gwp1RAEGedM9LONJZgA/pw7a9YdX5+4g5LfGvS6lyKhbMREKM/ZpCbzib33/eXPG312Y7vEpnWU8jWhrEYO18Zm5JSUinmOMW17O60kw6uloNdKYJAR6pjNWbRQ1HhH+8kn4i6+feHzzgH1eo8lLFE4KQGBiyLI4aa/ubE1oz5Xt+QzlyOHm2MMf2oS9Qfn4jsPrA9gZaMgeLtRF2Pczd8eFfXPQLwLVTohVvNyqAw9bCmsBaDTbPf1sd8Vd2z68cdZcqGqjLFHsaPMsCFkVewYWb6zkNtmOseMsbRfQ2Sw1i5K5F253hSCGqaLlBkSxrfrmqEYxobbqnRVVkIcTz1/+hvrZT0AKchLM3oI+I2XzrobuB8EJcKBljTW7gxq6rFBWpBxJXpRUJxGXSO1sxTsS6c3O/YfGTw+3fGQ/5b+RI4d75bUKR+6pBk92Yjs3Hp4rX54q//7xxL99+5ZfvHnkZ183fvFm4flZkVP1IlGiNInwqyWJ0OjcgIslnh7JCTeIW8HuMHClY9RYt14Oy8MqzWs8MNri+rlsyI9M7459E7RapN9HApDv2MmSeyIFP5P8GsTF3OFdhoes6BVbi7j3VBxcUhZvoBWhiOKsHFQ2DnrGqrCUykErNcIgrcvuy3tUkVgN3kJvEaWUgor/Kzh518mzUMwrF6ZLn7YjVpHoduuGnwNTncBbyiCvk9ECTDvoqSPBa8yHIzMcds/srd/v9XuDge5qiM3XJySVxgwM3qNIsvxl/7tL0a7FGH7vUFBdaF8KY4gYT34S55p1lQvfkcZCsj1zcaTiTqUcAnVkS8i4ZgcHuZWi/G3pUt0Fd4mMzhKEveLWigVLV214O5xYlwTDUI79qf21z3qZqCMVZEx3B03jkfpflAHFc8s4qLEED2HFv1PPIa4zY4IMh/iYDmZxXkHi7wwBOI3EN2lPke4LPJa3DCBzDXzGG+MaFzUU0srs7vwZKc03PmbrmhxKvnfh9jd3+2rUR4/zhvon+oANxZ88gLCiRL06oLtQx70MqwivEtjDY7GuosOdSQmnVa79uFjDY/s7LMX4dYV///zIL372c3760z+GmwIsRMIcglJ3WIoXGqq1hie58vD2EWuV+3uQtXSrUm7v0NsfsiyLxyzrM3oW9LCiqxcMMm0shwXbN8fum1v9KmdECjelcHp+RtbC6m4DzvtGPTdKpDxaw0FKUfZ9p+07KpV1KV4b4PFEeV3cO7MqLCtSw+tGZd/PUXhIetEmmno8ton7bwUoDTkKh/t72nbL+fyt8wpqiyqPxkpBnzeefvUbTvoNdy//EyfQbV+BvYFSkRVkUUqkOrphsSCszqzXSllWr4tQvHWzGrB7waVWBVkL3J4pcgtlwQ47L18d+entJ2xSOMqRfT1ysCNFD7TFaLXR9uYx47az1Y2n04lvzhu/+vaBf/3l1/zzr5/45180fvnlme1J6LGnDGdkD5DulswFNfai/1coAktUSrXIH3AQFqmZtgfADyUlQZvqtpJGTe6QLeZhBMgtFgZK1B3oTuTumch92MJbl4TzFOrzMVeGxhBC8Vx1nNI8Rq/N5WWzSosQkVvqkTnWXP94a6nWvXaDXJkgJTwBQfYtMSZO87FuLYlGmX3zpmBZ9Eo0PBUW6YFiXqumwGKFpRUOFVapqDQPd+V1xKLehvc7qObSbWbeeQjO5UrWS+jY7Pd4/d5gwJrHjXwxJKPxylTsqE+ZtNP0cZeSY+CYSCNdOM8TH6eM6w1Oqg3FENb8QEqpUD3/08FSiX/qSruXELVIHVn9GQd5gKw25wosnt3xWxBzYhpaAo640VTUKpGfHuVsw5XTPRGJYPqT5u6alVwopXx7Vowy4vu5QZxNOjwcOR6XajcxZ9x+n4uYg7C+LipDSo7nGHcf+7wVB1o9a9vScxKots9tWApTCOL9LxtALv4eYzE3O5o/H0/H9Ucy/5mCKmCWRR15JnebDoJhEjktwFySFlt0nXOXu897Mxvll5NaWmSEgkhPtQTT2LdgJ/6GKPaxdEBrJqCV1nYqC//hYedf/PIX/MMW9yqlk81MFk+pE7dKvVmhsh6PLMcTb7/csO2Bm/tbjuuR82lnub1hOb7wdEluUGns9cEJgcvC8faOdnrr6YAS7X+XI9vz2VsTUyh24O5wx2k/sePtWQ/HA1vdqTWquDX3VCzLLeuqtLrTto1Sn6nnM/L2Af3wQ3YRllLQ44FtdyFe0h1el7AdQso1h8mCgkYRqGYgZ/S4UI4vaG/eIHv1UrRBcbOnjeevv2F7fkP5/A85HF9j9ZG6P3lsd2nIckD1EEDQMzaEEvne3v9BlgNSFkRXV8ARMbVzoz4DxzPrQbD1gOjKIoW1vKA0J0myKG25Ab2liXKsjboAt9KjlPveeFUbn7YTf/Tpib/z+cf8F09v+Yu3j/yffv4N/9efPfCXv6m8eeNks2LFq1pegPD4vbvpfR/3BDFzT4KY91OxAAPQeshnyIFBxBVxD04yv4a3UQagFRC1KcttsnIi4yqNpL6vJ9nTv3Lhtc17SRmaCMP/dpe8sbRCY2ffnpDFP5DmPQm8bkhFDVaMpak366IisoTVPmStqgPy9M5qEMEa9FL0Xo/g2SssKjTbqCyIHBBbHW7ITovQnUNyz7corbDu4jwDPN23iHsLStdvDkWcxBne00x97gBwyMbLIPpvf30vzkDnsida68oxBy3g1Ls63VmYViKa4G2BR5c9G+pKpsUaJ/EUogjKi7f7RA2p5ulZE/qRxCIhXZsFGhP/KXKIgTvE+eKZ+qoNlI2MOt6UIKWYWyDxDW/CM0gzatGIqCiyJ8IFK74AA7J3KzpThHwfJPEwrtPTN2Nxh2UvmuSuuHVzMONA3Gdj7NpJlfY1nUsqlXQqtwRywfTP93N0zLpSTOCRLj2/l/FcMeExppdLzMx6CfJLXT4r/1mxxxqzVNzgleUE55DYhXx470sG8LGYb02NLRlKyOlPd2YK0eRTTByDrFBiofzNuqI3q2gNZm8CnlIcMCZ7vxbP/qq7F6gprs7aKoMnY04OdEFYYtMfQRqP28r/+B/+kv/dV9/w+v4VBzVaD394NkHddxDvo25BdFxuD6z3YE3Z6u49AUqjvHiFLvRYqOyFZfE13TZnZMsJt7QW9/DpIiyyULct5rKyV0VtpT3vNN1dgUuhtSfaXlkaSNmhrrSlYWVH99VDCkeFx28R+4y2LsiTh9p0WWj1iaUuVCrKRmtefVDKArY5Y7/c+NpqBhycpLY0dH1N4y/dNVubW/lt4/zmTHt6hLsD+uFnaDnQ9rc0vkLKCdbFOzhKQWxxpWXBujcB2ZzroBGWoUTxJt/z+w7tVGHZWbillIUkVTdVb7pUvLOr1orZI0WUiueli4l7FmRlWRfsAKvdcBDlcHvm1enEDz965o8++oJ/9JOv+b//8i3/419+w//r1xtv3+zUcxQ8wqAVBK8RQM2waQOplPTWNmgB9ERS+bekAQAhd6k0iTRIFFWLJJjo8qnegCjzpzrI7fJFfa0Tfu6Gz5XEPrI5M0C6niHbxHv/OH9pmjkB1oMGYYgX5TnHMyxAq5QTSN1gKZgqtXiYoLSNpZ1hU+p5gXZCqusbDU+f6YKqtxxWso8F7A2q4MC2Pfpar0a1Sl0i84UDZgdKu/H9vzR2iaJGGKstnNuK6o5qZSnCKp5+6KbVGbVjyKTq1+vhYi7kVdea+v9p7+x5IymCMPx09eyOjXWyRGKJkBRSEv5/REBCQkAACacLkPBJd+ud/iKo7plZaw+O9PZ9nFjeWe/291vVVd3D2/7aaPo0/+tugmHxXYqNOl7uz4wGvEb7xO/+Zl+TNvt+U5BDUvbgtrXDjAnbDzR1BUuvJA8QGR3Sx2HPIugb7F6RdRXNjnFxP8HFqjUUdVgtYV9BxvfuVuEowlhouvtta5UhffrBRDuhvOqstU7Drol3tbWzNNvFtsbnMazjtqvi122yuv5f9aYLL0NzVRxsYo3j6Aq/1pHGeOXzx7bLXkDsyrY9tzXA6+8S+iC45mG4yHQY3eQK3ky2eRsuX/GysOsLwJYN0meghudd4+7Vcc3peEPooqVYIc+N9OYMbw60h0a2THxJtBMcPty7sXSxhbGLZyl+CU4x49e3b/nt9z/44el7avZ+H/p59z5cfN/a1quXYX6Yme5nYvBLi2iBYzsSHuZ++FWDMI4O92yhmhdfYFvr3t5erzFytEhqrac7BU+5igfqV/ekWKiHCYsHajgBlfxSqOnM8lwgvTDHaU2hjGHifEqEj4npGHeiLZCLn2OARUoulJRpYeq3OCRo0SP8a/DbQ4NhYXKLPU5ejnLqx4MUlo8nyjljFKbHJ+LDIxko6QWjuBUbIU7ukvUMAncir+Mk4N6kHgeyrVvV081Kj/g2j02wMXE3tmDecoI1PXHrby3MEKPHmAR3sXvUuNGIxPkeOx45lDu+nR/45vGJ774+8ePTMz+9+4uf/3zPL+/OPP9dKMs6cfe5MLBdaR7WPXQLYZ1Dfal2seCf2+fe3ZyxNwSuTE/b2GPEN8GYF/ynb3f2UbMuZNf+1zVx0P8+0uyG0eRjEiIFoxIXtxRyaJ7adzDC2Q/ICpNRLVHrmSOVmBukSs7FT+csbHVkng1go1wNavEjuHOupLyQ8gdSes8SFkI8EsNCbRMt3NHaTG4FzLDJj8GiRuxwh9WE2eLCpJ9mG6xga7jHtm6MdqibCvDVZLdUtZFuODLqPlMMhPbvvlohhBBCfOHYfz8ihBBCiC8ZiQEhhBDixpEYEEIIIW4ciQEhhBDixpEYEEIIIW4ciQEhhBDixpEYEEIIIW4ciQEhhBDixpEYEEIIIW6cfwBR4p466zZKlwAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-17T07:58:36.959984Z",
     "start_time": "2025-09-17T07:58:36.946046Z"
    }
   },
   "cell_type": "code",
   "source": [
    "gray_image =  (0.299*image[...,0] + 0.587*image[...,1] + 0.114*image[...,2]).astype(np.float32)  # 彩色图片并转为灰度\n",
    "gray_image.shape"
   ],
   "id": "729633a9cbd5f82f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(427, 640)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-17T07:58:37.086385Z",
     "start_time": "2025-09-17T07:58:36.977007Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.imshow(gray_image,cmap=\"gray\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ],
   "id": "4240ce8135b88803",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-17T08:40:31.240813Z",
     "start_time": "2025-09-17T08:40:31.181737Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 构建一个3*3的卷积窗口（滤波器）\n",
    "\n",
    "K_vertical = np.array([[ 1,  0, -1],\n",
    "                       [ 1,  0, -1],\n",
    "                       [ 1,  0, -1]], dtype=np.float32)\n",
    "\n",
    "K_horizontal = np.array([[ 1,  1,  1],\n",
    "                         [ 0,  0,  0],\n",
    "                         [-1, -1, -1]], dtype=np.float32)\n",
    "\n",
    "\n",
    "# 构建 7*7的卷积窗口\n",
    "d = np.array([-3, -2, -1, 0, 1, 2, 3], dtype=np.float32)\n",
    "s = np.array([ 1,  6, 15,20,15, 6, 1], dtype=np.float32)\n",
    "K_vertical_fancy  = np.outer(s, d)   # 7x7 垂直边缘检测核\n",
    "K_horizontal_fancy = K_vertical_fancy.T             # 7x7 水平边缘核\n",
    "\n",
    "edge_v = correlate2d(gray_image, K_vertical_fancy, mode='valid')  # mode=\"valid\" 不做任何填充\n",
    "edge_h = convolve2d(gray_image, K_horizontal_fancy, mode='valid')"
   ],
   "id": "6271a58ebc647d82",
   "outputs": [],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-17T08:40:32.378320Z",
     "start_time": "2025-09-17T08:40:32.193916Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(121)\n",
    "plt.imshow(edge_v, cmap=\"gray\")\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"vertical edge\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.imshow(edge_h, cmap=\"gray\")\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"horizontal edge\")\n",
    "\n",
    "plt.show()"
   ],
   "id": "404d711af38d58c7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "|上面例子可以看出，可以自己构造滤波器（filter）去对图片做卷积操作，实现对图片的特征提取（垂直边缘检测/水平边缘检测）， 在神经网络中，滤波器的参数是通过反向传播去更新优化的，除了初始化，不需要去自己指定；所以它在优化过程中会根据任务学会怎么提取图片的特征",
   "id": "6fac2bfcef207332"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Padding（填充）",
   "id": "e81c8345193e82ea"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "- 卷积中的 Padding（填充）\n",
    "\n",
    "在卷积神经网络中，卷积操作通常会使输入图像的尺寸缩小。例如，一个 $6 \\times 6$ 的图像与一个 $3 \\times 3$ 卷积核做卷积，得到的输出是 $4 \\times 4$。一般公式为：\n",
    "\n",
    "$$\n",
    "\\text{输出大小} = n - f + 1\n",
    "$$\n",
    "\n",
    "其中 $n$ 是输入大小，$f$ 是卷积核大小。\n",
    "\n",
    "- 不使用填充的问题\n",
    "\n",
    "1. **尺寸不断缩小**：每次卷积都会减少图像的边长，深层网络中图像可能很快缩小到无法使用。\n",
    "2. **边缘信息损失**：图像边缘或角落的像素在卷积中被利用的次数远少于中心区域，导致边缘特征信息被“弱化”。\n",
    "\n",
    "- 填充的解决方案\n",
    "\n",
    "为了解决上述问题，可以在输入的四周补上一圈像素（通常为 0），即 **padding**。\n",
    "\n",
    "* 例如，将 $6 \\times 6$ 的输入补一圈（p=1），变为 $8 \\times 8$，再用 $3 \\times 3$ 卷积，就能得到与原输入相同大小的 $6 \\times 6$ 输出。\n",
    "* 一般公式为：\n",
    "\n",
    "$$\n",
    "\\text{输出大小} = n + 2p - f + 1\n",
    "$$\n",
    "\n",
    "- 两种常见的卷积方式\n",
    "\n",
    "1. **Valid 卷积**：不做填充 ($p=0$)，输出尺寸会变小。\n",
    "2. **Same 卷积**：选择合适的填充，使输出与输入保持相同大小。\n",
    "\n",
    "   * 当卷积核大小 $f$ 为奇数时，所需填充量为：\n",
    "\n",
    "   $$\n",
    "   p = \\frac{f-1}{2}\n",
    "   $$\n",
    "\n",
    "   例如，$f=3$ 时，$p=1$；$f=5$ 时，$p=2$。\n",
    "\n",
    "- 为什么卷积核常取奇数大小\n",
    "\n",
    "* 奇数核有自然的中心像素点，方便定义卷积的“中心”。\n",
    "* 避免了左右/上下不对称填充的麻烦。\n",
    "* 因此常见的卷积核有 $3 \\times 3$、$5 \\times 5$、$7 \\times 7$ 等。\n",
    "\n",
    "---\n",
    "\n",
    "总结：**Padding 的作用是避免卷积后图像过快缩小，同时保留边缘信息。Valid 卷积不填充，输出更小；Same 卷积自动填充，保证输出与输入同尺寸。**\n",
    "\n",
    "\n"
   ],
   "id": "abd092b1f2278de"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-17T08:29:37.998936Z",
     "start_time": "2025-09-17T08:29:37.987762Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# p =1，  p=？\n",
    "# 随堂练习：思考下padding怎么用numpy实现 自己代码尝试\n",
    "\n",
    "\n",
    "some_data = np.array([[3,0,1,2,7,4],\n",
    "                      [1,5,8,9,3,1],\n",
    "                      [2,7,2,5,1,3],\n",
    "                      [0,1,3,1,7,8],\n",
    "                      [4,2,1,6,2,8],\n",
    "                      [2,4,5,2,3,9]])\n",
    "\n",
    "np.pad(some_data, (1,2))"
   ],
   "id": "f86b76e80f020c4c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 3, 0, 1, 2, 7, 4, 0, 0],\n",
       "       [0, 1, 5, 8, 9, 3, 1, 0, 0],\n",
       "       [0, 2, 7, 2, 5, 1, 3, 0, 0],\n",
       "       [0, 0, 1, 3, 1, 7, 8, 0, 0],\n",
       "       [0, 4, 2, 1, 6, 2, 8, 0, 0],\n",
       "       [0, 2, 4, 5, 2, 3, 9, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Stride（步幅）",
   "id": "8b468db363f96336"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "- 卷积中的 Stride（步幅）\n",
    "\n",
    "在卷积操作中，**stride**（步幅）表示卷积核每次移动的距离：\n",
    "\n",
    "* **stride = 1**：卷积核逐像素滑动，输出尺寸较大。\n",
    "* **stride > 1**：卷积核“跳着走”，每次跨过多个像素，输出尺寸明显缩小。\n",
    "\n",
    "- 输出大小的公式\n",
    "\n",
    "若输入是 $n \\times n$，卷积核大小为 $f \\times f$，填充为 $p$，步幅为 $s$，则输出大小为：\n",
    "\n",
    "$$\n",
    "\\text{输出尺寸} = \\left\\lfloor \\frac{n + 2p - f}{s} \\right\\rfloor + 1\n",
    "$$\n",
    "\n",
    "其中：\n",
    "\n",
    "* $n$：输入大小\n",
    "* $f$：卷积核大小\n",
    "* $p$：padding（填充大小）\n",
    "* $s$：stride（步幅大小）\n",
    "* $\\lfloor \\cdot \\rfloor$：向下取整（floor）\n",
    "\n",
    "- 举例\n",
    "\n",
    "* 输入 $7 \\times 7$，卷积核 $3 \\times 3$，无填充 ($p=0$)：\n",
    "\n",
    "  * 当 $s=1$ → 输出大小：$(7 - 3)/1 + 1 = 5$，即 $5 \\times 5$。\n",
    "  * 当 $s=2$ → 输出大小：$\\lfloor (7 - 3)/2 \\rfloor + 1 = 3$，即 $3 \\times 3$。\n",
    "\n",
    "- 作用总结\n",
    "\n",
    "1. **控制输出特征图的大小**：步幅越大，输出越小。\n",
    "2. **减少计算量**：更少的位置参与卷积运算。\n",
    "3. **调节特征提取粒度**：小步幅 → 细致特征；大步幅 → 粗略特征。\n",
    "\n",
    "---\n",
    "\n",
    "总结：\n",
    "**Stride 决定卷积核每次移动的步长，公式 $\\lfloor (n + 2p - f)/s \\rfloor + 1$ 可以计算输出大小。步幅越大，特征图越小。**\n",
    "\n"
   ],
   "id": "a6a1f9cc3b5794ea"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 在三维数组（体积）上卷积",
   "id": "da36e58bfa66aa80"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": " ![可视化在体积上卷积](./images/CNN/p5.png)",
   "id": "7a42dda5505778de"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "- 为什么需要体积卷积\n",
    "\n",
    "在灰度图像中，输入通常是二维矩阵，例如 $6 \\times 6$。\n",
    "但在彩色图像中，数据包含 **三个通道（RGB）**，所以图像可以表示为 **$6 \\times 6 \\times 3$**。\n",
    "\n",
    "* 前两个维度：高度和宽度\n",
    "* 第三个维度：通道数（channels）\n",
    "\n",
    "这类输入就不再是一个平面，而是一个 **体积（Volume）**。\n",
    "\n",
    "\n",
    "- 三维卷积核（filter）\n",
    "\n",
    "为了处理多通道图像，卷积核本身也必须是三维的。\n",
    "\n",
    "* 例如输入是 $6 \\times 6 \\times 3$，卷积核可以是 **$3 \\times 3 \\times 3$**。\n",
    "* **注意：卷积核的通道数必须与输入一致**，即这里的“3”必须相等。\n",
    "\n",
    "一个 $3 \\times 3 \\times 3$ 卷积核包含 $27$ 个参数。\n",
    "\n",
    "卷积的过程：\n",
    "\n",
    "1. 把卷积核放在输入的一个位置上（覆盖 $3 \\times 3 \\times 3$ 的小立方块）。\n",
    "2. 对应元素逐一相乘（27 次乘法），再相加，得到一个标量。\n",
    "3. 把卷积核滑动到下一个位置，重复以上步骤。\n",
    "4. 最终得到一个二维矩阵（例如 $4 \\times 4$）。\n",
    "\n",
    "结果：\n",
    "\n",
    "$$\n",
    "6 \\times 6 \\times 3 \\;\\;\\;\\ast\\;\\;\\; 3 \\times 3 \\times 3 \\;\\;\\;\\rightarrow\\;\\;\\; 4 \\times 4 \\times 1\n",
    "$$\n",
    "\n",
    "\n",
    "\n",
    "- 不同的卷积核 → 不同的特征\n",
    "\n",
    "卷积核的参数决定了它能检测的特征。\n",
    "\n",
    "* **只检测红色通道的边缘**：卷积核在红色通道放置边缘检测模板，其他通道全为零。\n",
    "* **检测任意颜色的边缘**：卷积核在 RGB 三个通道都放置相同的边缘检测模板。\n",
    "\n",
    "通过这种方式，不同的卷积核可以提取不同特征：垂直边缘、水平边缘、对角线纹理、颜色变化等。\n",
    "\n",
    "\n",
    "- 多个卷积核（filters）\n",
    "\n",
    "在实际的卷积层中，我们不会只用一个卷积核，而是会用 **多个卷积核** 来同时检测不同特征。\n",
    "\n",
    "* 每个卷积核产生一个二维输出（feature map）。\n",
    "* 所有输出 **按通道堆叠**，形成一个新的体积。\n",
    "\n",
    "例如：\n",
    "\n",
    "* 输入：$6 \\times 6 \\times 3$\n",
    "* 使用 2 个 $3 \\times 3 \\times 3$ 卷积核\n",
    "* 输出：$4 \\times 4 \\times 2$\n",
    "\n",
    "公式：\n",
    "\n",
    "$$\n",
    "n \\times n \\times n_C \\;\\;\\;\\ast\\;\\;\\; f \\times f \\times n_C \\;\\;\\;\\rightarrow\\;\\;\\; (n-f+1) \\times (n-f+1) \\times n_C'\n",
    "$$\n",
    "\n",
    "其中：\n",
    "\n",
    "* $n$：输入宽/高\n",
    "* $n_C$：输入通道数（必须与卷积核通道一致）\n",
    "* $f$：卷积核宽/高\n",
    "* $n_C'$：卷积核个数（决定输出通道数）\n",
    "\n",
    "（这里假设 stride=1，padding=0）\n",
    "\n",
    "- 术语说明\n",
    "\n",
    "* **Channels（通道数）**：输入或输出的第三个维度。\n",
    "* **Depth（深度）**：有时文献里用来表示通道数，但容易与“网络深度”混淆，所以更推荐用“通道数”。\n",
    "\n",
    "---\n",
    "\n",
    "- 总结\n",
    "\n",
    "1. 卷积不仅能在二维图像上操作，还能作用于多通道输入（体积）。\n",
    "2. 卷积核必须与输入的通道数一致。\n",
    "3. 每个卷积核会输出一个二维特征图，多个卷积核输出的结果会堆叠成新的体积。\n",
    "4. 输出体积的通道数 = 使用的卷积核数。\n",
    "\n",
    "**体积卷积让我们能在多通道图像上提取特征，每个卷积核生成一个特征图，多个卷积核堆叠后形成新的输出体积。**"
   ],
   "id": "94663c1daeea1476"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 单个卷积层",
   "id": "161719d71ae9c011"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "1. 输入与输出\n",
    "\n",
    "* **输入体积（Input）**：\n",
    "\n",
    "  $$\n",
    "  n_H^{[l-1]} \\times n_W^{[l-1]} \\times n_C^{[l-1]}\n",
    "  $$\n",
    "\n",
    "  高度 × 宽度 × 通道数\n",
    "\n",
    "* **输出体积（Output）**：\n",
    "\n",
    "  $$\n",
    "  n_H^{[l]} \\times n_W^{[l]} \\times n_C^{[l]}\n",
    "  $$\n",
    "\n",
    "  其中：\n",
    "\n",
    "  * 高度与宽度由公式决定\n",
    "  * 深度（通道数）等于 **卷积核个数**\n",
    "\n",
    "\n",
    "2. 卷积层的关键参数\n",
    "\n",
    "* $f^{[l]}$：filter size（卷积核大小）\n",
    "* $p^{[l]}$：padding（填充）\n",
    "* $s^{[l]}$：stride（步幅）\n",
    "* $n_C^{[l]}$：number of filters（卷积核个数 = 输出通道数）\n",
    "\n",
    "\n",
    "\n",
    "3. 卷积运算\n",
    "\n",
    "* **每个卷积核** 大小为：\n",
    "\n",
    "  $$\n",
    "  f^{[l]} \\times f^{[l]} \\times n_C^{[l-1]}\n",
    "  $$\n",
    "\n",
    "* 卷积核在输入上滑动，计算：\n",
    "\n",
    "  $$\n",
    "  Z^{[l]} = W^{[l]} * A^{[l-1]} + b^{[l]}\n",
    "  $$\n",
    "\n",
    "  * $W^{[l]}$：卷积核权重\n",
    "  * $A^{[l-1]}$：前一层的激活\n",
    "  * $b^{[l]}$：偏置\n",
    "\n",
    "\n",
    "4. 输出尺寸公式\n",
    "\n",
    "输出的高度/宽度：\n",
    "\n",
    "$$\n",
    "n_H^{[l]} = \\left\\lfloor \\frac{n_H^{[l-1]} + 2p^{[l]} - f^{[l]}}{s^{[l]}} \\right\\rfloor + 1\n",
    "$$\n",
    "\n",
    "$$\n",
    "n_W^{[l]} = \\left\\lfloor \\frac{n_W^{[l-1]} + 2p^{[l]} - f^{[l]}}{s^{[l]}} \\right\\rfloor + 1\n",
    "$$\n",
    "\n",
    "输出通道数：\n",
    "\n",
    "$$\n",
    "n_C^{[l]} = \\text{卷积核个数}\n",
    "$$\n",
    "\n",
    "\n",
    "\n",
    "5. 参数数量\n",
    "\n",
    "* 每个卷积核参数量：\n",
    "\n",
    "  $$\n",
    "  f^{[l]} \\times f^{[l]} \\times n_C^{[l-1]} + 1 \\quad (\\text{+1 表示偏置})\n",
    "  $$\n",
    "* 总参数量：\n",
    "\n",
    "  $$\n",
    "  \\left(f^{[l]} \\times f^{[l]} \\times n_C^{[l-1]} + 1\\right) \\times n_C^{[l]}\n",
    "  $$\n",
    "\n",
    "**例子：**\n",
    "\n",
    "* 输入：$6 \\times 6 \\times 3$\n",
    "* 卷积核：$3 \\times 3 \\times 3$，共 10 个\n",
    "* 每个卷积核：$27 + 1 = 28$\n",
    "* 总参数量：$28 \\times 10 = 280$\n",
    "\n",
    "\n",
    "\n",
    "6. 测试\n",
    "\n",
    "**题目：**\n",
    "输入是 $32 \\times 32 \\times 3$，使用 16 个 $5 \\times 5 \\times 3$ 卷积核，每个核带 1 个偏置。\n",
    "请计算参数总数是多少？\n",
    "\n",
    "\n",
    "7. 小结\n",
    "\n",
    "单个卷积层完成的流程：\n",
    "\n",
    "1. 输入体积与卷积核滑动相乘并加偏置\n",
    "2. 应用非线性激活函数（如 ReLU）\n",
    "3. 输出新的体积，通道数 = 卷积核个数\n",
    "4. 参数数量与 **卷积核大小、数量、输入通道数** 有关，与输入图像尺寸无关\n",
    "\n"
   ],
   "id": "5bcf90ebaea04bf7"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-17T09:06:34.459256Z",
     "start_time": "2025-09-17T09:06:34.447920Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 批次数量：m\n",
    "#  输入：m * 32*32*3\n",
    "\n",
    "#  卷积层： 卷积核： f*f*3*卷积核的数量(n_c),  f*f*3*n_c\n",
    "#  输出（不带偏置）的形状   m * （32-f+1）* （32-f+1） * n_c\n",
    "#    偏置的形状 ：        1 *   1      *    1      * n_c\n",
    "\n",
    "#  输出的形状（带上偏置）：  m * （32-f+1）* （32-f+1） * n_c\n",
    "\n",
    "#   Flatten：           m * （（32-f+1）* （32-f+1） * n_c = 把卷积层的单个样本输出， 直接摊平成 一维度的）\n",
    "#   Dense(10, activation = \"softmax\")\n",
    "\n",
    "\n",
    "# 1. 搭一个带有卷积层的神经网络 tf.keras.layers.Conv2D()\n",
    "# 输入 -> 卷积 -> 摊平 -> 全连接\n",
    "\n",
    "# 2. 去手写数字/时尚衣服/彩图数据 试一下，能不能训练\n"
   ],
   "id": "60e40ee0dfe4584",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1216"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 内存需求",
   "id": "e7b855aa37318683"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "1. 训练期间卷积层需要大量内存\n",
    "\n",
    "CNN 的一个挑战是卷积层需要大量的 RAM，在训练期间尤其如此，因为反向传播需要在前向传播过程中计算出的所有中间值。\n",
    "\n",
    "例如，考虑一个具有 200 个 $5 \\times 5$ 滤波器、步幅为 1 且采用 \"same\" 填充的卷积层。\n",
    "如果输入是 $150 \\times 100$ 的 RGB 图像（3 个通道），则参数数量为：\n",
    "\n",
    "$$\n",
    "(5 \\times 5 \\times 3 + 1) \\times 200 = 15200\n",
    "$$\n",
    "\n",
    "（+1 表示偏置项）。与全连接层相比，它很小。\n",
    "\n",
    "然而，200 个特征图中的每个特征图都包含 $150 \\times 100$ 个神经元，\n",
    "并且每个神经元都需要计算 $5 \\times 5 \\times 3 = 75$ 个输入的加权和：\n",
    "\n",
    "$$\n",
    "总共 = 2.25 亿次浮点运算\n",
    "$$\n",
    "\n",
    "虽然不如全连接层那么糟糕，但仍然需要进行大量的计算。\n",
    "\n",
    "此外，如果使用 32 位浮点数来表示神经元，则单层占用的 RAM 为：\n",
    "\n",
    "$$\n",
    "200 \\times 150 \\times 100 \\times 32 \\text{ 位} = 9600 \\text{ 万位} \\approx 12 \\text{ MB}\n",
    "$$\n",
    "\n",
    "这还只是一个实例，如果训练 100 个实例，则会占用约 **1.2 GB RAM**！\n",
    "\n",
    "\n",
    "2. 推理期间与训练期间的区别\n",
    "\n",
    "* **推理期间（即对新实例进行预测时）**：\n",
    "  只需要计算完一层后，就可以释放前一层占用的 RAM，因此只需要两个连续层所需的 RAM。\n",
    "\n",
    "* **训练期间**：\n",
    "  需要保留前向传播过程中计算出的所有中间值以便进行反向传播，因此训练期间的 RAM 需求 = **所有层所需 RAM 的总量**。\n",
    "\n",
    "\n",
    "3. 内存优化方法\n",
    "\n",
    "* 如果训练因内存不足而崩溃，可以尝试：\n",
    "\n",
    "  * 减小批量大小（batch size）\n",
    "  * 使用步幅参数来降维，删除一些层\n",
    "  * 使用 16 位浮点数而不是 32 位浮点数\n",
    "  * 或将 CNN的网络层 分布在多个设备上\n",
    "\n"
   ],
   "id": "5cffc41efc9eba92"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## 卷积神经网络示例",
   "id": "2b4f443a24b55d15"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "**输入：** $ 39 \\times 39 \\times 3 $\n",
    "\n",
    "- 第 1 层卷积\n",
    "\n",
    "* 卷积核大小：$f^{[1]} = 3$\n",
    "* 步幅：$s^{[1]} = 1$\n",
    "* 填充：$p^{[1]} = 0$\n",
    "* 卷积核个数：10\n",
    "\n",
    "**输出尺寸：** $ 37 \\times 37 \\times 10 $\n",
    "\n",
    "\n",
    "- 第 2 层卷积\n",
    "\n",
    "* 卷积核大小：$f^{[2]} = 5$\n",
    "* 步幅：$s^{[2]} = 2$\n",
    "* 填充：$p^{[2]} = 0$\n",
    "* 卷积核个数：20\n",
    "\n",
    "**输出尺寸：** $ 17 \\times 17 \\times 20 $\n",
    "\n",
    "- 第 3 层卷积\n",
    "\n",
    "* 卷积核大小：$f^{[3]} = 5$\n",
    "* 步幅：$s^{[3]} = 2$\n",
    "* 填充：$p^{[3]} = 0$\n",
    "* 卷积核个数：40\n",
    "\n",
    "**输出尺寸：** $ 7 \\times 7 \\times 40 $\n",
    "\n",
    "- Flatten层: $ 7 \\times 7 \\times 40 = 1960 $\n",
    "\n",
    "- 输出层\n",
    "* 全连接层，输入维度 = 1960\n",
    "* 输出维度 = 分类数 $K$\n",
    "* 激活函数：\n",
    "\n",
    "  * **逻辑回归 (logistic sigmoid)** → 二分类\n",
    "  * **Softmax** → 多分类\n",
    "\n",
    "\n",
    "- 总结：\n",
    "这个网络结构是一个**ConvNet 示例**：输入图像 → 卷积层（3 层）→ Flatten → 全连接输出层（logistic/softmax）。"
   ],
   "id": "5cd63464d4e3b05d"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 池化",
   "id": "b44f19ec18629e90"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "<img alt=\"Max Pooling示意图\" height=\"500\" src=\"./images/CNN/p6.png\" width=\"500\"/>\n",
    "\n",
    "一旦了解了卷积层如何工作，池化层就很容易掌握。它们的目标是对输入图像进行下采样（即缩小操作），以便减少计算量，内存使用量和参数数量（也能降低过拟合的风险）\n",
    "\n"
   ],
   "id": "4cd5fac568a3e26f"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "在卷积神经网络（ConvNet）中，除了卷积层和全连接层，还常用 **池化层**。\n",
    "\n",
    "它的主要作用是：\n",
    "\n",
    "* **缩小特征图的尺寸**（降低计算量和内存消耗）\n",
    "* **提高特征的鲁棒性**（让检测到的特征不依赖于具体位置）\n",
    "\n",
    "1. Max Pooling（最大池化）\n",
    "\n",
    "**过程：**\n",
    "\n",
    "* 将输入划分为若干小区域（由滤波器大小 $f$ 和步幅 $s$ 决定）\n",
    "* 对每个区域取最大值，作为输出\n",
    "\n",
    "**示例 1：**\n",
    "输入 $4 \\times 4$，使用 $f=2, s=2$ 的 max pooling：\n",
    "\n",
    "* 输出为 $2 \\times 2$，每个位置等于对应 $2 \\times 2$ 区域中的最大值\n",
    "\n",
    "**示例 2：**\n",
    "输入 $5 \\times 5$，使用 $f=3, s=1$：\n",
    "\n",
    "* 输出为 $3 \\times 3$，每个输出位置是对应 $3 \\times 3$ 区域的最大值\n",
    "\n",
    "**特点：**\n",
    "\n",
    "* 如果一个区域里有某个特征被强烈激活（值大），max pooling 会保留下来\n",
    "* 直观理解：只要特征在区域内被检测到，就能传递下去\n",
    "\n",
    "\n",
    "2. Average Pooling（平均池化）\n",
    "\n",
    "* 与 max pooling 类似，但计算区域内的 **平均值**\n",
    "* 现在较少使用，但在网络最后阶段，有时会用 average pooling 来压缩表示，例如从 $7 \\times 7 \\times 1000$ → $1 \\times 1 \\times 1000$\n",
    "\n",
    "\n",
    "3. 高维输入的池化\n",
    "\n",
    "* 输入可以是三维体积（例如 $H \\times W \\times C$）\n",
    "* 池化操作对每个 **通道（channel）独立**进行\n",
    "* 所以池化层不会改变通道数：\n",
    "\n",
    "  $$\n",
    "  输入: n_H \\times n_W \\times n_C \\;\\;\\rightarrow\\;\\; 输出: n_H' \\times n_W' \\times n_C\n",
    "  $$\n",
    "\n",
    "\n",
    "4. 超参数（Hyperparameters）\n",
    "\n",
    "* **滤波器大小 $f$**：常用 $f=2$\n",
    "* **步幅 $s$**：常用 $s=2$\n",
    "* **填充 $p$**：几乎总是 $p=0$\n",
    "* **类型**：max pooling 或 average pooling\n",
    "\n",
    "常见配置：\n",
    "\n",
    "* $f=2, s=2$：将特征图长宽缩小一半\n",
    "* $f=3, s=2$：缩小得更多\n",
    "\n",
    "5. 池化层的特点\n",
    "\n",
    "* **没有可学习的参数**：\n",
    "\n",
    "  * 卷积层有权重和偏置需要训练\n",
    "  * 池化层只有固定的超参数（$f, s, p$），无参数需要梯度下降更新\n",
    "* **固定计算**：只是应用 max 或 average 运算，不会被训练过程改变\n",
    "\n",
    "\n",
    "\n",
    "6. 总结\n",
    "\n",
    "* **Max pooling**：取区域内最大值，常用，能保留显著特征\n",
    "* **Average pooling**：取区域内平均值，较少用，但在最后阶段有时会使用\n",
    "* **池化层不会改变通道数，只缩小空间尺寸**\n",
    "* **没有可学习参数**，只是固定函数\n",
    "\n",
    "**池化层就是在空间上压缩特征图，减少计算和参数量，同时保留关键信息。**"
   ],
   "id": "617201c97fc25065"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 典型CNN架构",
   "id": "9c13c73c06c96929"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "典型的CNN架构是先堆叠一些卷积层（通常每个卷积层都跟随一个ReLU层），接着放一个池化层，然后再堆叠另外几个卷积层(+ReLU)，再放另一个池化层，以此类推。随着图像不断经过卷积网络的各层，图像变得越来越小，但由于卷积层的存在，图像通常也越来越深（即具有更多的通道）。在顶部，添加一个常规前馈神经网络，该网络由几个全连接层(+ReLU)组成，最后一层（例如输出估计类别概率的softmax层）输出预测结果。\n",
    "\n",
    "与其使用具有5×5核的卷积层，不如堆叠两层具有3×3核的卷积层：它使用较少的参数且需要较少的计算量，并且通常性能会更好。第一个卷积层是一个例外：它可以典型地具有较大的核（例如5×5），步幅通常为2或更大，这将减小图像的空间维度而不会丢失太多信息，由于输入图像通常只有3个通道，因此它不需要太多的计算量。"
   ],
   "id": "37f7d14abfe2430d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T06:25:09.431799Z",
     "start_time": "2025-09-18T06:25:08.976363Z"
    }
   },
   "cell_type": "code",
   "source": [
    "mnist = tf.keras.datasets.fashion_mnist.load_data()\n",
    "(X_train_full, y_train_full), (X_test, y_test) = mnist\n",
    "X_train_full = np.expand_dims(X_train_full, axis=-1).astype(np.float32) / 255  # expand_dims增加通道维度\n",
    "X_test = np.expand_dims(X_test.astype(np.float32), axis=-1) / 255\n",
    "X_train, X_valid = X_train_full[:-5000], X_train_full[-5000:]\n",
    "y_train, y_valid = y_train_full[:-5000], y_train_full[-5000:]"
   ],
   "id": "874b192443a44e6c",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T06:25:13.939551Z",
     "start_time": "2025-09-18T06:25:12.616632Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from functools import partial\n",
    "\n",
    "DefaultConv2D = partial(tf.keras.layers.Conv2D, kernel_size=3, padding=\"same\",\n",
    "                        activation=\"relu\", kernel_initializer=\"he_normal\")\n",
    "\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    DefaultConv2D(filters=64, kernel_size=7, input_shape=[28, 28, 1]),   # 输入图片是灰度图，只有一个颜色通道，当加载数据时，确保每个图片都是[28,28,1]\n",
    "    tf.keras.layers.MaxPool2D(),\n",
    "    DefaultConv2D(filters=128),\n",
    "    DefaultConv2D(filters=128),\n",
    "    tf.keras.layers.MaxPool2D(),\n",
    "    DefaultConv2D(filters=256),\n",
    "    DefaultConv2D(filters=256),\n",
    "    tf.keras.layers.MaxPool2D(),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(units=128, activation=\"relu\",\n",
    "                          kernel_initializer=\"he_normal\"),\n",
    "    tf.keras.layers.Dropout(0.5),\n",
    "    tf.keras.layers.Dense(units=64, activation=\"relu\",\n",
    "                          kernel_initializer=\"he_normal\"),\n",
    "    tf.keras.layers.Dropout(0.5),\n",
    "    tf.keras.layers.Dense(units=10, activation=\"softmax\")\n",
    "])"
   ],
   "id": "38be5c242e63adf9",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T06:25:19.908721Z",
     "start_time": "2025-09-18T06:25:19.865615Z"
    }
   },
   "cell_type": "code",
   "source": "model.summary()",
   "id": "ed6898d0f0f4c332",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 28, 28, 64)        3200      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 14, 14, 64)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 14, 14, 128)       73856     \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 14, 14, 128)       147584    \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 7, 7, 128)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 7, 7, 256)         295168    \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 7, 7, 256)         590080    \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPoolin  (None, 3, 3, 256)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2304)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               295040    \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1413834 (5.39 MB)\n",
      "Trainable params: 1413834 (5.39 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "使用functools.partial()函数来定义DefaultConv2D，它的行为与Conv2D类似，但具有不同的默认参数：核大小为3、\"same\"、ReLU激活函数及相应的He初始化。\n",
    "\n",
    "接下来，创建Sequential模型。它的第一层是DefaultConv2D，带有64个相当大的滤波器(7×7)。它使用默认步幅1，因为输入图像不是很大。它还设置input_shape=[28，28，1]，因为图像有28×28像素，具有一个颜色通道（即灰度）。当加载Fashion MNIST数据集时，请确保每个图像都具有此形状：可能需要使用np.reshape()或np.expanddims()添加通道维度。或者，可以使用Reshape层作为模型的第一层。\n",
    "\n",
    "然后，添加一个使用默认池大小2的最大池化层，因此它将每个空间维度除以因子2。\n",
    "\n",
    "重复相同的结构两次：两个卷积层后跟一个最大池化层。对于较大的图像，可以重复多次此结构，重复次数是可以调整的超参数。\n",
    "\n",
    "注意，随着CNN向输出层延伸，滤波器的数量会翻倍（最初是64，然后是128，再然后是256）：这种增长是有意义的，因为低层特征的数量通常很少（例如小圆圈、水平线），但是有很多不同的方法可以将它们组合成更高层次的特征。通常的做法是在每个池化层之后将滤波器的数量加倍：由于池化层将每个空间维度除以2，因此我们能负担得起下一层特征图数量的加倍而不必担心参数数量、内存使用量或计算量的暴增。\n",
    "\n",
    "接下来是全连接网络，由两个隐藏密集层和一个密集输出层组成。由于它是一个有10个类别的分类任务，因此输出层有10个单元，并且它使用softmax激活函数。请注意，必须在第一个密集层之前展平输入，因为它需要每个实例的一维特征数组。还添加了两个dropout层（每个层的dropout率为50%）以降低过拟合的风险。"
   ],
   "id": "a2f29744aaa99311"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T06:54:58.279266Z",
     "start_time": "2025-09-18T06:31:55.208297Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\",\n",
    "              metrics=[\"accuracy\"])\n",
    "history = model.fit(X_train, y_train, epochs=10,\n",
    "                    validation_data=(X_valid, y_valid))\n",
    "score = model.evaluate(X_test, y_test)\n",
    "\n",
    "X_new = X_test[:10]  # pretend new images\n",
    "y_pred = model.predict(X_new)"
   ],
   "id": "2157503bb03f13af",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1719/1719 [==============================] - 140s 79ms/step - loss: 0.7367 - accuracy: 0.7428 - val_loss: 0.3901 - val_accuracy: 0.8738\n",
      "Epoch 2/10\n",
      "1719/1719 [==============================] - 135s 79ms/step - loss: 0.4129 - accuracy: 0.8608 - val_loss: 0.3083 - val_accuracy: 0.8850\n",
      "Epoch 3/10\n",
      "1719/1719 [==============================] - 136s 79ms/step - loss: 0.3596 - accuracy: 0.8825 - val_loss: 0.2871 - val_accuracy: 0.8958\n",
      "Epoch 4/10\n",
      "1719/1719 [==============================] - 137s 79ms/step - loss: 0.3165 - accuracy: 0.8923 - val_loss: 0.2906 - val_accuracy: 0.8948\n",
      "Epoch 5/10\n",
      "1719/1719 [==============================] - 136s 79ms/step - loss: 0.2930 - accuracy: 0.9007 - val_loss: 0.2583 - val_accuracy: 0.9054\n",
      "Epoch 6/10\n",
      "1719/1719 [==============================] - 136s 79ms/step - loss: 0.2735 - accuracy: 0.9071 - val_loss: 0.2653 - val_accuracy: 0.9020\n",
      "Epoch 7/10\n",
      "1719/1719 [==============================] - 136s 79ms/step - loss: 0.2599 - accuracy: 0.9125 - val_loss: 0.2574 - val_accuracy: 0.9106\n",
      "Epoch 8/10\n",
      "1719/1719 [==============================] - 136s 79ms/step - loss: 0.2451 - accuracy: 0.9181 - val_loss: 0.2789 - val_accuracy: 0.9064\n",
      "Epoch 9/10\n",
      "1719/1719 [==============================] - 137s 79ms/step - loss: 0.2369 - accuracy: 0.9193 - val_loss: 0.2833 - val_accuracy: 0.9058\n",
      "Epoch 10/10\n",
      "1719/1719 [==============================] - 146s 85ms/step - loss: 0.2249 - accuracy: 0.9233 - val_loss: 0.2573 - val_accuracy: 0.9084\n",
      "313/313 [==============================] - 6s 20ms/step - loss: 0.2856 - accuracy: 0.9059\n",
      "1/1 [==============================] - 0s 128ms/step\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 为什么要CNN",
   "id": "3bc02fa8a4e35575"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "1. 参数共享（Parameter Sharing）\n",
    "\n",
    "* **直观理解**：一个边缘检测器（比如竖直边缘的 3×3 卷积核），在图像左上角和右下角都可能有用。\n",
    "* **意义**：不需要在每个位置都学习独立的参数，而是让同一个卷积核在整张图像上滑动使用。\n",
    "* **结果**：显著减少参数数量。例如，一个 32×32×3 的输入，如果直接全连接到下一层需要上千万参数，而卷积层可能只需要几百个。\n",
    "\n",
    "2. 稀疏连接（Sparsity of Connections）\n",
    "\n",
    "* 在卷积中，每个输出神经元只与输入中的局部区域（感受野）相连，而不是和所有像素都相连。\n",
    "* **优点**：减少计算量，提升模型学习的效率，同时减少过拟合的风险。\n",
    "\n",
    "3. 具备平移不变性（Translation Invariance）\n",
    "\n",
    "* 一只猫即使向右平移几个像素，依然是“猫”。\n",
    "* 由于卷积核在整张图像上共享并滑动，CNN 天然具备对小幅度平移的鲁棒性。\n",
    "* 这让 CNN 更适合处理现实场景中存在的图像位置变化。\n",
    "\n",
    "4. 更易训练，更适合图像任务\n",
    "\n",
    "* 参数量减少 → 可以在更小的数据集上训练 → 不容易过拟合。\n",
    "* 层次化结构 → 前几层学习边缘、纹理等低级特征，后几层逐渐组合成更复杂的形状和语义（如“眼睛”“猫脸”）。\n",
    "* 适合搭配池化层（Pooling）与全连接层，形成完整的图像识别系统。\n",
    "\n",
    "\n",
    "**总结**\n",
    "CNN 的核心优势在于：\n",
    "\n",
    "* **参数共享** → 大幅减少参数量\n",
    "* **稀疏连接** → 计算和存储高效\n",
    "* **平移不变性** → 识别鲁棒性更强\n",
    "\n",
    "因此 CNN 在计算机视觉任务（图像分类、目标检测、风格迁移等）中成为主流方法。"
   ],
   "id": "9ed5142d505837ae"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### CNN应用--随堂练习",
   "id": "6c889985cc09e832"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### 微笑识别",
   "id": "456413d79c79a0e1"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:35:26.468735Z",
     "start_time": "2025-09-18T08:35:26.456888Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import h5py\n",
    "from cnn_utils import *\n",
    "\n",
    "def load_happy_dataset():\n",
    "    train_dataset = h5py.File('datasets/train_happy.h5', \"r\")\n",
    "    train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\n",
    "    train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\n",
    "\n",
    "    test_dataset = h5py.File('datasets/test_happy.h5', \"r\")\n",
    "    test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\n",
    "    test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\n",
    "\n",
    "    classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\n",
    "\n",
    "    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n",
    "    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n",
    "    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes"
   ],
   "id": "cc52f73e10829080",
   "outputs": [],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:35:26.872294Z",
     "start_time": "2025-09-18T08:35:26.802930Z"
    }
   },
   "cell_type": "code",
   "source": [
    "X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_happy_dataset()\n",
    "\n",
    "# Normalize image vectors\n",
    "X_train = X_train_orig/255.\n",
    "X_test = X_test_orig/255.\n",
    "\n",
    "# Reshape\n",
    "Y_train = Y_train_orig.T\n",
    "Y_test = Y_test_orig.T\n",
    "\n",
    "print (\"number of training examples = \" + str(X_train.shape[0]))\n",
    "print (\"number of test examples = \" + str(X_test.shape[0]))\n",
    "print (\"X_train shape: \" + str(X_train.shape))\n",
    "print (\"Y_train shape: \" + str(Y_train.shape))\n",
    "print (\"X_test shape: \" + str(X_test.shape))\n",
    "print (\"Y_test shape: \" + str(Y_test.shape))"
   ],
   "id": "a1dfa6ee310d1339",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of training examples = 600\n",
      "number of test examples = 150\n",
      "X_train shape: (600, 64, 64, 3)\n",
      "Y_train shape: (600, 1)\n",
      "X_test shape: (150, 64, 64, 3)\n",
      "Y_test shape: (150, 1)\n"
     ]
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T07:03:43.443028Z",
     "start_time": "2025-09-18T07:03:43.431446Z"
    }
   },
   "cell_type": "code",
   "source": "classes",
   "id": "282e8050d27efe46",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T07:03:33.729831Z",
     "start_time": "2025-09-18T07:03:33.578688Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "index = 1\n",
    "plt.imshow(X_train_orig[index]) #display sample training image\n",
    "plt.show()"
   ],
   "id": "65817a1543a65e94",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:33:27.526849Z",
     "start_time": "2025-09-18T08:33:27.503088Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def happyModel():\n",
    "    \"\"\"\n",
    "    Implements the forward propagation for the binary classification model:\n",
    "    ZEROPAD2D -> CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> FLATTEN -> DENSE\n",
    "\n",
    "    Note that for simplicity and grading purposes, you'll hard-code all the values\n",
    "    such as the stride and kernel (filter) sizes.\n",
    "    Normally, functions should take these values as function parameters.\n",
    "\n",
    "    Arguments:\n",
    "    None\n",
    "\n",
    "    Returns:\n",
    "    model -- TF Keras model (object containing the information for the entire training process)\n",
    "    \"\"\"\n",
    "    model = tf.keras.Sequential([\n",
    "            # YOUR CODE STARTS HERE\n",
    "            ## ZeroPadding2D with padding 3, input shape of 64 x 64 x 3\n",
    "            tf.keras.layers.ZeroPadding2D((3, 3), input_shape=(64,64,3)),\n",
    "            ## Conv2D with 32 7x7 filters and stride of 1\n",
    "            tf.keras.layers.Conv2D(filters=32, kernel_size=(7, 7), strides=1, padding=\"valid\"),\n",
    "            ## BatchNormalization for axis 3\n",
    "            tf.keras.layers.BatchNormalization(axis=3),\n",
    "            ## ReLU\n",
    "            tf.keras.layers.ReLU(),\n",
    "            ## Max Pooling 2D with default parameters\n",
    "            tf.keras.layers.MaxPool2D(),\n",
    "            ## Flatten layer\n",
    "            tf.keras.layers.Flatten(),\n",
    "            ## Dense layer with 1 unit for output & 'sigmoid' activation\n",
    "            tf.keras.layers.Dense(units=1, activation=\"sigmoid\"),\n",
    "            # YOUR CODE ENDS HERE\n",
    "        ])\n",
    "    return model"
   ],
   "id": "c2167910bf8186ce",
   "outputs": [],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:33:29.958145Z",
     "start_time": "2025-09-18T08:33:29.746998Z"
    }
   },
   "cell_type": "code",
   "source": [
    "happy_model = happyModel()\n",
    "# Print a summary for each layer\n",
    "for layer in summary(happy_model):\n",
    "    print(layer)\n",
    "\n",
    "output = [['ZeroPadding2D', (None, 70, 70, 3), 0, ((3, 3), (3, 3))],\n",
    "            ['Conv2D', (None, 64, 64, 32), 4736, 'valid', 'linear', 'GlorotUniform'],\n",
    "            ['BatchNormalization', (None, 64, 64, 32), 128],\n",
    "            ['ReLU', (None, 64, 64, 32), 0],\n",
    "            ['MaxPooling2D', (None, 32, 32, 32), 0, (2, 2), (2, 2), 'valid'],\n",
    "            ['Flatten', (None, 32768), 0],\n",
    "            ['Dense', (None, 1), 32769, 'sigmoid']]\n",
    "\n",
    "comparator(summary(happy_model), output)"
   ],
   "id": "6bc72bdb51af4df",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ZeroPadding2D', (None, 70, 70, 3), 0, ((3, 3), (3, 3))]\n",
      "['Conv2D', (None, 64, 64, 32), 4736, 'valid', 'linear', 'GlorotUniform']\n",
      "['BatchNormalization', (None, 64, 64, 32), 128]\n",
      "['ReLU', (None, 64, 64, 32), 0]\n",
      "['MaxPooling2D', (None, 32, 32, 32), 0, (2, 2), (2, 2), 'valid']\n",
      "['Flatten', (None, 32768), 0]\n",
      "['Dense', (None, 1), 32769, 'sigmoid']\n",
      "\u001B[32mAll tests passed!\u001B[0m\n"
     ]
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:35:08.550256Z",
     "start_time": "2025-09-18T08:35:08.537963Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# todo: 编译happy model， 注意这个是二元分类，损失函数\n",
    "happy_model.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])"
   ],
   "id": "d1bded738f2acc20",
   "outputs": [],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:35:09.221609Z",
     "start_time": "2025-09-18T08:35:09.180880Z"
    }
   },
   "cell_type": "code",
   "source": "happy_model.summary()",
   "id": "22fed48a9a28e724",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " zero_padding2d (ZeroPaddin  (None, 70, 70, 3)         0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 64, 64, 32)        4736      \n",
      "                                                                 \n",
      " batch_normalization (Batch  (None, 64, 64, 32)        128       \n",
      " Normalization)                                                  \n",
      "                                                                 \n",
      " re_lu (ReLU)                (None, 64, 64, 32)        0         \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPoolin  (None, 32, 32, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 32768)             0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 32769     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 37633 (147.00 KB)\n",
      "Trainable params: 37569 (146.75 KB)\n",
      "Non-trainable params: 64 (256.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:36:08.102935Z",
     "start_time": "2025-09-18T08:35:51.113390Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# todo：训练模型，10个轮次，批量大小16\n",
    "happy_model.fit(X_train, Y_train, epochs=10, batch_size=16)"
   ],
   "id": "3166a117b44e8198",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "38/38 [==============================] - 3s 39ms/step - loss: 0.8347 - accuracy: 0.7217\n",
      "Epoch 2/10\n",
      "38/38 [==============================] - 2s 40ms/step - loss: 0.1617 - accuracy: 0.9367\n",
      "Epoch 3/10\n",
      "38/38 [==============================] - 1s 38ms/step - loss: 0.1029 - accuracy: 0.9633\n",
      "Epoch 4/10\n",
      "38/38 [==============================] - 1s 39ms/step - loss: 0.0767 - accuracy: 0.9783\n",
      "Epoch 5/10\n",
      "38/38 [==============================] - 1s 39ms/step - loss: 0.0723 - accuracy: 0.9683\n",
      "Epoch 6/10\n",
      "38/38 [==============================] - 1s 39ms/step - loss: 0.0616 - accuracy: 0.9833\n",
      "Epoch 7/10\n",
      "38/38 [==============================] - 1s 38ms/step - loss: 0.0529 - accuracy: 0.9867\n",
      "Epoch 8/10\n",
      "38/38 [==============================] - 2s 47ms/step - loss: 0.0424 - accuracy: 0.9833\n",
      "Epoch 9/10\n",
      "38/38 [==============================] - 1s 39ms/step - loss: 0.0480 - accuracy: 0.9867\n",
      "Epoch 10/10\n",
      "38/38 [==============================] - 2s 41ms/step - loss: 0.0485 - accuracy: 0.9883\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1b3d4fb1480>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:37:33.148552Z",
     "start_time": "2025-09-18T08:37:32.700884Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# todo：在测试集上评估模型\n",
    "happy_model.evaluate(X_test, Y_test)"
   ],
   "id": "cdddd0e971e9c185",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 21ms/step - loss: 0.1972 - accuracy: 0.9333\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.19717752933502197, 0.9333333373069763]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### 手势识别",
   "id": "21e56cf82638f5bf"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:37:52.051222Z",
     "start_time": "2025-09-18T08:37:52.016691Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def load_signs_dataset():\n",
    "    train_dataset = h5py.File('datasets/train_signs.h5', \"r\")\n",
    "    train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\n",
    "    train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\n",
    "\n",
    "    test_dataset = h5py.File('datasets/test_signs.h5', \"r\")\n",
    "    test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\n",
    "    test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\n",
    "\n",
    "    classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\n",
    "\n",
    "    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n",
    "    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n",
    "\n",
    "    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\n",
    "\n",
    "\n",
    "X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_signs_dataset()"
   ],
   "id": "305e05ca7c89155c",
   "outputs": [],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T07:19:43.946740Z",
     "start_time": "2025-09-18T07:19:43.796386Z"
    }
   },
   "cell_type": "code",
   "source": [
    "index = 1\n",
    "plt.imshow(X_train_orig[index])\n",
    "print (\"y = \" + str(np.squeeze(Y_train_orig[:, index])))"
   ],
   "id": "bdbaea1bc5dbffaa",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T07:05:36.728800Z",
     "start_time": "2025-09-18T07:05:36.716240Z"
    }
   },
   "cell_type": "code",
   "source": "classes",
   "id": "8d54a019100a30d2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:38:06.727485Z",
     "start_time": "2025-09-18T08:38:06.664055Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def convert_to_one_hot(Y, C):\n",
    "    Y = np.eye(C)[Y.reshape(-1)].T\n",
    "    return Y\n",
    "\n",
    "X_train = X_train_orig/255.\n",
    "X_test = X_test_orig/255.\n",
    "Y_train = convert_to_one_hot(Y_train_orig, 6).T\n",
    "Y_test = convert_to_one_hot(Y_test_orig, 6).T\n",
    "print (\"number of training examples = \" + str(X_train.shape[0]))\n",
    "print (\"number of test examples = \" + str(X_test.shape[0]))\n",
    "print (\"X_train shape: \" + str(X_train.shape))\n",
    "print (\"Y_train shape: \" + str(Y_train.shape))\n",
    "print (\"X_test shape: \" + str(X_test.shape))\n",
    "print (\"Y_test shape: \" + str(Y_test.shape))"
   ],
   "id": "a6d3f3d948340ab1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of training examples = 1080\n",
      "number of test examples = 120\n",
      "X_train shape: (1080, 64, 64, 3)\n",
      "Y_train shape: (1080, 6)\n",
      "X_test shape: (120, 64, 64, 3)\n",
      "Y_test shape: (120, 6)\n"
     ]
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:43:25.372552Z",
     "start_time": "2025-09-18T08:43:25.361056Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def convolutional_model(input_shape):\n",
    "    \"\"\"\n",
    "    Implements the forward propagation for the model:\n",
    "    CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> DENSE\n",
    "\n",
    "    Note that for simplicity and grading purposes, you'll hard-code some values\n",
    "    such as the stride and kernel (filter) sizes.\n",
    "    Normally, functions should take these values as function parameters.\n",
    "\n",
    "    Arguments:\n",
    "    input_img -- input dataset, of shape (input_shape)\n",
    "\n",
    "    Returns:\n",
    "    model -- TF Keras model (object containing the information for the entire training process)\n",
    "    \"\"\"\n",
    "    ## CONV2D: 8 filters 4x4, stride of 1, padding 'SAME'\n",
    "    # Z1 = None\n",
    "    ## RELU\n",
    "    # A1 = None\n",
    "    ## MAXPOOL: window 8x8, stride 8, padding 'SAME'\n",
    "    # P1 = None\n",
    "    ## CONV2D: 16 filters 2x2, stride 1, padding 'SAME'\n",
    "    # Z2 = None\n",
    "    ## RELU\n",
    "    # A2 = None\n",
    "    ## MAXPOOL: window 4x4, stride 4, padding 'SAME'\n",
    "    # P2 = None\n",
    "    ## FLATTEN\n",
    "    # F = None\n",
    "    ## Dense layer\n",
    "    ## 6 neurons in output layer. Hint: one of the arguments should be \"activation='softmax'\"\n",
    "    # outputs = None\n",
    "    # YOUR CODE STARTS HERE\n",
    "    input_img = tf.keras.layers.Input(input_shape)\n",
    "    Z1 = tf.keras.layers.Conv2D(filters=8, kernel_size=(4,4), strides=1, padding=\"same\")(input_img)\n",
    "    A1 = tf.keras.layers.ReLU()(Z1)\n",
    "    P1 = tf.keras.layers.MaxPool2D(pool_size=(8,8), padding=\"same\")(A1)\n",
    "\n",
    "    Z2 = tf.keras.layers.Conv2D(filters=16, kernel_size=(2,2), strides=1, padding=\"same\")(P1)\n",
    "    A2 = tf.keras.layers.ReLU()(Z2)\n",
    "    P2 = tf.keras.layers.MaxPool2D(pool_size=(4,4), padding=\"same\")(A2)\n",
    "    F = tf.keras.layers.Flatten()(P2)\n",
    "    output = tf.keras.layers.Dense(6, activation=\"softmax\")(F)\n",
    "    model = tf.keras.Model(inputs=input_img, outputs=output)\n",
    "\n",
    "    # YOUR CODE ENDS HERE\n",
    "\n",
    "    return model"
   ],
   "id": "871a27d1742f3088",
   "outputs": [],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:43:26.262458Z",
     "start_time": "2025-09-18T08:43:26.157505Z"
    }
   },
   "cell_type": "code",
   "source": [
    "conv_model = convolutional_model((64, 64, 3))\n",
    "conv_model.compile(optimizer='adam',\n",
    "                  loss='categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "conv_model.summary()\n",
    "\n",
    "output = [['InputLayer', [(None, 64, 64, 3)], 0],\n",
    "        ['Conv2D', (None, 64, 64, 8), 392, 'same', 'linear', 'GlorotUniform'],\n",
    "        ['ReLU', (None, 64, 64, 8), 0],\n",
    "        ['MaxPooling2D', (None, 8, 8, 8), 0, (8, 8), (8, 8), 'same'],\n",
    "        ['Conv2D', (None, 8, 8, 16), 528, 'same', 'linear', 'GlorotUniform'],\n",
    "        ['ReLU', (None, 8, 8, 16), 0],\n",
    "        ['MaxPooling2D', (None, 2, 2, 16), 0, (4, 4), (4, 4), 'same'],\n",
    "        ['Flatten', (None, 64), 0],\n",
    "        ['Dense', (None, 6), 390, 'softmax']]\n",
    "\n",
    "comparator(summary(conv_model), output)"
   ],
   "id": "189e49afe484bdf0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 64, 64, 3)]       0         \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 64, 64, 8)         392       \n",
      "                                                                 \n",
      " re_lu_1 (ReLU)              (None, 64, 64, 8)         0         \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPoolin  (None, 8, 8, 8)           0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 8, 8, 16)          528       \n",
      "                                                                 \n",
      " re_lu_2 (ReLU)              (None, 8, 8, 16)          0         \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPoolin  (None, 2, 2, 16)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 6)                 390       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1310 (5.12 KB)\n",
      "Trainable params: 1310 (5.12 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "\u001B[32mAll tests passed!\u001B[0m\n"
     ]
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:49:47.281733Z",
     "start_time": "2025-09-18T08:48:55.577694Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_dataset = tf.data.Dataset.from_tensor_slices((X_train, Y_train)).batch(64)  # todo: 使用tf.data.Dataset.from_tensor_slices 打包训练特征和标签，并指定批量大小64\n",
    "valid_dataset = tf.data.Dataset.from_tensor_slices((X_test, Y_test)).batch(64)  # todo: 使用tf.data.Dataset.from_tensor_slices 打包测试特征和标签，并指定批量大小64\n",
    "history = conv_model.fit(train_dataset, epochs=100, validation_data=valid_dataset)"
   ],
   "id": "4b2c4f184edd3ee7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "17/17 [==============================] - 1s 36ms/step - loss: 1.8031 - accuracy: 0.1657 - val_loss: 1.7892 - val_accuracy: 0.2333\n",
      "Epoch 2/100\n",
      "17/17 [==============================] - 1s 29ms/step - loss: 1.7890 - accuracy: 0.1907 - val_loss: 1.7848 - val_accuracy: 0.2500\n",
      "Epoch 3/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 1.7849 - accuracy: 0.2435 - val_loss: 1.7798 - val_accuracy: 0.3000\n",
      "Epoch 4/100\n",
      "17/17 [==============================] - 1s 37ms/step - loss: 1.7800 - accuracy: 0.2731 - val_loss: 1.7742 - val_accuracy: 0.3333\n",
      "Epoch 5/100\n",
      "17/17 [==============================] - 1s 33ms/step - loss: 1.7747 - accuracy: 0.3046 - val_loss: 1.7680 - val_accuracy: 0.3833\n",
      "Epoch 6/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 1.7684 - accuracy: 0.3157 - val_loss: 1.7597 - val_accuracy: 0.3917\n",
      "Epoch 7/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 1.7602 - accuracy: 0.3074 - val_loss: 1.7485 - val_accuracy: 0.4000\n",
      "Epoch 8/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 1.7489 - accuracy: 0.3481 - val_loss: 1.7353 - val_accuracy: 0.4250\n",
      "Epoch 9/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.7339 - accuracy: 0.3435 - val_loss: 1.7144 - val_accuracy: 0.4583\n",
      "Epoch 10/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.7139 - accuracy: 0.3528 - val_loss: 1.6832 - val_accuracy: 0.4083\n",
      "Epoch 11/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.6844 - accuracy: 0.3648 - val_loss: 1.6472 - val_accuracy: 0.4583\n",
      "Epoch 12/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.6516 - accuracy: 0.3907 - val_loss: 1.6053 - val_accuracy: 0.4667\n",
      "Epoch 13/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.6084 - accuracy: 0.4194 - val_loss: 1.5502 - val_accuracy: 0.5000\n",
      "Epoch 14/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.5620 - accuracy: 0.4454 - val_loss: 1.4995 - val_accuracy: 0.5417\n",
      "Epoch 15/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.5169 - accuracy: 0.4620 - val_loss: 1.4470 - val_accuracy: 0.5250\n",
      "Epoch 16/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 1.4696 - accuracy: 0.4852 - val_loss: 1.3999 - val_accuracy: 0.5500\n",
      "Epoch 17/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 1.4240 - accuracy: 0.5111 - val_loss: 1.3561 - val_accuracy: 0.5917\n",
      "Epoch 18/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.3792 - accuracy: 0.5259 - val_loss: 1.3170 - val_accuracy: 0.5750\n",
      "Epoch 19/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.3367 - accuracy: 0.5500 - val_loss: 1.2789 - val_accuracy: 0.6000\n",
      "Epoch 20/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.2967 - accuracy: 0.5722 - val_loss: 1.2416 - val_accuracy: 0.6417\n",
      "Epoch 21/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.2586 - accuracy: 0.5815 - val_loss: 1.2100 - val_accuracy: 0.6417\n",
      "Epoch 22/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.2227 - accuracy: 0.5889 - val_loss: 1.1792 - val_accuracy: 0.6500\n",
      "Epoch 23/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.1889 - accuracy: 0.6074 - val_loss: 1.1528 - val_accuracy: 0.6417\n",
      "Epoch 24/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.1573 - accuracy: 0.6130 - val_loss: 1.1255 - val_accuracy: 0.6583\n",
      "Epoch 25/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.1277 - accuracy: 0.6222 - val_loss: 1.1023 - val_accuracy: 0.6583\n",
      "Epoch 26/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.1003 - accuracy: 0.6407 - val_loss: 1.0801 - val_accuracy: 0.6583\n",
      "Epoch 27/100\n",
      "17/17 [==============================] - 0s 28ms/step - loss: 1.0746 - accuracy: 0.6565 - val_loss: 1.0580 - val_accuracy: 0.6500\n",
      "Epoch 28/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 1.0505 - accuracy: 0.6556 - val_loss: 1.0379 - val_accuracy: 0.6500\n",
      "Epoch 29/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 1.0279 - accuracy: 0.6667 - val_loss: 1.0183 - val_accuracy: 0.6583\n",
      "Epoch 30/100\n",
      "17/17 [==============================] - 1s 32ms/step - loss: 1.0067 - accuracy: 0.6731 - val_loss: 1.0006 - val_accuracy: 0.6750\n",
      "Epoch 31/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.9866 - accuracy: 0.6778 - val_loss: 0.9836 - val_accuracy: 0.6917\n",
      "Epoch 32/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.9679 - accuracy: 0.6815 - val_loss: 0.9662 - val_accuracy: 0.6917\n",
      "Epoch 33/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.9502 - accuracy: 0.6861 - val_loss: 0.9508 - val_accuracy: 0.6917\n",
      "Epoch 34/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.9339 - accuracy: 0.6907 - val_loss: 0.9357 - val_accuracy: 0.7000\n",
      "Epoch 35/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.9180 - accuracy: 0.6954 - val_loss: 0.9226 - val_accuracy: 0.6917\n",
      "Epoch 36/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.9031 - accuracy: 0.7028 - val_loss: 0.9087 - val_accuracy: 0.6917\n",
      "Epoch 37/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.8886 - accuracy: 0.7083 - val_loss: 0.8957 - val_accuracy: 0.6917\n",
      "Epoch 38/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.8743 - accuracy: 0.7130 - val_loss: 0.8833 - val_accuracy: 0.7083\n",
      "Epoch 39/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.8608 - accuracy: 0.7213 - val_loss: 0.8706 - val_accuracy: 0.7083\n",
      "Epoch 40/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.8481 - accuracy: 0.7241 - val_loss: 0.8588 - val_accuracy: 0.7000\n",
      "Epoch 41/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.8360 - accuracy: 0.7343 - val_loss: 0.8486 - val_accuracy: 0.6917\n",
      "Epoch 42/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.8243 - accuracy: 0.7389 - val_loss: 0.8377 - val_accuracy: 0.7000\n",
      "Epoch 43/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.8131 - accuracy: 0.7491 - val_loss: 0.8280 - val_accuracy: 0.7000\n",
      "Epoch 44/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.8025 - accuracy: 0.7528 - val_loss: 0.8180 - val_accuracy: 0.7083\n",
      "Epoch 45/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.7920 - accuracy: 0.7546 - val_loss: 0.8098 - val_accuracy: 0.7083\n",
      "Epoch 46/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.7820 - accuracy: 0.7583 - val_loss: 0.7993 - val_accuracy: 0.7083\n",
      "Epoch 47/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.7720 - accuracy: 0.7620 - val_loss: 0.7913 - val_accuracy: 0.7083\n",
      "Epoch 48/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.7624 - accuracy: 0.7620 - val_loss: 0.7832 - val_accuracy: 0.7083\n",
      "Epoch 49/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.7532 - accuracy: 0.7620 - val_loss: 0.7754 - val_accuracy: 0.7083\n",
      "Epoch 50/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.7442 - accuracy: 0.7630 - val_loss: 0.7669 - val_accuracy: 0.7083\n",
      "Epoch 51/100\n",
      "17/17 [==============================] - 0s 30ms/step - loss: 0.7353 - accuracy: 0.7630 - val_loss: 0.7584 - val_accuracy: 0.7083\n",
      "Epoch 52/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.7265 - accuracy: 0.7639 - val_loss: 0.7504 - val_accuracy: 0.7167\n",
      "Epoch 53/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.7179 - accuracy: 0.7685 - val_loss: 0.7434 - val_accuracy: 0.7250\n",
      "Epoch 54/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.7097 - accuracy: 0.7704 - val_loss: 0.7355 - val_accuracy: 0.7250\n",
      "Epoch 55/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.7016 - accuracy: 0.7769 - val_loss: 0.7282 - val_accuracy: 0.7333\n",
      "Epoch 56/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.6933 - accuracy: 0.7806 - val_loss: 0.7222 - val_accuracy: 0.7250\n",
      "Epoch 57/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.6854 - accuracy: 0.7861 - val_loss: 0.7146 - val_accuracy: 0.7417\n",
      "Epoch 58/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.6775 - accuracy: 0.7880 - val_loss: 0.7074 - val_accuracy: 0.7500\n",
      "Epoch 59/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.6696 - accuracy: 0.7907 - val_loss: 0.7006 - val_accuracy: 0.7417\n",
      "Epoch 60/100\n",
      "17/17 [==============================] - 1s 31ms/step - loss: 0.6617 - accuracy: 0.7926 - val_loss: 0.6937 - val_accuracy: 0.7417\n",
      "Epoch 61/100\n",
      "17/17 [==============================] - 1s 33ms/step - loss: 0.6544 - accuracy: 0.7944 - val_loss: 0.6873 - val_accuracy: 0.7500\n",
      "Epoch 62/100\n",
      "17/17 [==============================] - 1s 38ms/step - loss: 0.6470 - accuracy: 0.7944 - val_loss: 0.6803 - val_accuracy: 0.7583\n",
      "Epoch 63/100\n",
      "17/17 [==============================] - 1s 32ms/step - loss: 0.6398 - accuracy: 0.7954 - val_loss: 0.6739 - val_accuracy: 0.7667\n",
      "Epoch 64/100\n",
      "17/17 [==============================] - 1s 31ms/step - loss: 0.6329 - accuracy: 0.7981 - val_loss: 0.6675 - val_accuracy: 0.7667\n",
      "Epoch 65/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.6259 - accuracy: 0.7981 - val_loss: 0.6617 - val_accuracy: 0.7750\n",
      "Epoch 66/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.6193 - accuracy: 0.8009 - val_loss: 0.6556 - val_accuracy: 0.7750\n",
      "Epoch 67/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.6127 - accuracy: 0.8056 - val_loss: 0.6503 - val_accuracy: 0.7750\n",
      "Epoch 68/100\n",
      "17/17 [==============================] - 1s 32ms/step - loss: 0.6063 - accuracy: 0.8102 - val_loss: 0.6451 - val_accuracy: 0.7833\n",
      "Epoch 69/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.5998 - accuracy: 0.8093 - val_loss: 0.6386 - val_accuracy: 0.7917\n",
      "Epoch 70/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.5933 - accuracy: 0.8102 - val_loss: 0.6336 - val_accuracy: 0.8000\n",
      "Epoch 71/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.5868 - accuracy: 0.8130 - val_loss: 0.6281 - val_accuracy: 0.8083\n",
      "Epoch 72/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.5806 - accuracy: 0.8185 - val_loss: 0.6223 - val_accuracy: 0.8000\n",
      "Epoch 73/100\n",
      "17/17 [==============================] - 1s 29ms/step - loss: 0.5743 - accuracy: 0.8222 - val_loss: 0.6168 - val_accuracy: 0.8083\n",
      "Epoch 74/100\n",
      "17/17 [==============================] - 1s 29ms/step - loss: 0.5682 - accuracy: 0.8231 - val_loss: 0.6108 - val_accuracy: 0.8083\n",
      "Epoch 75/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.5620 - accuracy: 0.8250 - val_loss: 0.6065 - val_accuracy: 0.8083\n",
      "Epoch 76/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.5559 - accuracy: 0.8278 - val_loss: 0.6009 - val_accuracy: 0.8083\n",
      "Epoch 77/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.5498 - accuracy: 0.8287 - val_loss: 0.5954 - val_accuracy: 0.8083\n",
      "Epoch 78/100\n",
      "17/17 [==============================] - 1s 32ms/step - loss: 0.5436 - accuracy: 0.8324 - val_loss: 0.5896 - val_accuracy: 0.8000\n",
      "Epoch 79/100\n",
      "17/17 [==============================] - 1s 32ms/step - loss: 0.5376 - accuracy: 0.8380 - val_loss: 0.5845 - val_accuracy: 0.8000\n",
      "Epoch 80/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.5316 - accuracy: 0.8426 - val_loss: 0.5795 - val_accuracy: 0.8000\n",
      "Epoch 81/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.5259 - accuracy: 0.8463 - val_loss: 0.5745 - val_accuracy: 0.8000\n",
      "Epoch 82/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.5198 - accuracy: 0.8472 - val_loss: 0.5717 - val_accuracy: 0.7917\n",
      "Epoch 83/100\n",
      "17/17 [==============================] - 1s 31ms/step - loss: 0.5139 - accuracy: 0.8491 - val_loss: 0.5659 - val_accuracy: 0.8000\n",
      "Epoch 84/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.5078 - accuracy: 0.8519 - val_loss: 0.5627 - val_accuracy: 0.8000\n",
      "Epoch 85/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.5021 - accuracy: 0.8537 - val_loss: 0.5585 - val_accuracy: 0.8000\n",
      "Epoch 86/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.4963 - accuracy: 0.8593 - val_loss: 0.5529 - val_accuracy: 0.8000\n",
      "Epoch 87/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.4907 - accuracy: 0.8611 - val_loss: 0.5498 - val_accuracy: 0.8000\n",
      "Epoch 88/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.4857 - accuracy: 0.8620 - val_loss: 0.5449 - val_accuracy: 0.7833\n",
      "Epoch 89/100\n",
      "17/17 [==============================] - 1s 31ms/step - loss: 0.4802 - accuracy: 0.8648 - val_loss: 0.5400 - val_accuracy: 0.7833\n",
      "Epoch 90/100\n",
      "17/17 [==============================] - 1s 29ms/step - loss: 0.4749 - accuracy: 0.8667 - val_loss: 0.5363 - val_accuracy: 0.8083\n",
      "Epoch 91/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.4698 - accuracy: 0.8676 - val_loss: 0.5325 - val_accuracy: 0.8083\n",
      "Epoch 92/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.4650 - accuracy: 0.8676 - val_loss: 0.5290 - val_accuracy: 0.8083\n",
      "Epoch 93/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.4601 - accuracy: 0.8667 - val_loss: 0.5256 - val_accuracy: 0.8083\n",
      "Epoch 94/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.4554 - accuracy: 0.8685 - val_loss: 0.5219 - val_accuracy: 0.8083\n",
      "Epoch 95/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.4506 - accuracy: 0.8685 - val_loss: 0.5191 - val_accuracy: 0.8083\n",
      "Epoch 96/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.4457 - accuracy: 0.8713 - val_loss: 0.5152 - val_accuracy: 0.8083\n",
      "Epoch 97/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.4409 - accuracy: 0.8750 - val_loss: 0.5129 - val_accuracy: 0.8167\n",
      "Epoch 98/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.4364 - accuracy: 0.8750 - val_loss: 0.5097 - val_accuracy: 0.8167\n",
      "Epoch 99/100\n",
      "17/17 [==============================] - 0s 29ms/step - loss: 0.4318 - accuracy: 0.8750 - val_loss: 0.5064 - val_accuracy: 0.8167\n",
      "Epoch 100/100\n",
      "17/17 [==============================] - 1s 30ms/step - loss: 0.4273 - accuracy: 0.8750 - val_loss: 0.5033 - val_accuracy: 0.8167\n"
     ]
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-18T08:49:51.357044Z",
     "start_time": "2025-09-18T08:49:50.642748Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "df_loss_acc = pd.DataFrame(history.history)\n",
    "df_loss= df_loss_acc[['loss','val_loss']].copy()\n",
    "df_loss.rename(columns={'loss':'train','val_loss':'validation'},inplace=True)\n",
    "\n",
    "df_acc= df_loss_acc[['accuracy','val_accuracy']].copy()\n",
    "df_acc.rename(columns={'accuracy':'train','val_accuracy':'validation'},inplace=True)\n",
    "\n",
    "df_loss.plot(title='Model loss',figsize=(12,8)).set(xlabel='Epoch',ylabel='Loss')\n",
    "df_acc.plot(title='Model Accuracy',figsize=(12,8)).set(xlabel='Epoch',ylabel='Accuracy')\n",
    "\n",
    "plt.show()"
   ],
   "id": "40e3894bc3642dfc",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## Numpy实现卷积神经网络",
   "id": "af0b471b8112314c"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "\n",
    "这是一个 **自己动手理解并实现卷积操作的练习**。\n",
    "\n",
    "1. **不用 AI 直接写答案** —— 因为我已经用过 AI 生成过代码，知道它会怎么写。现在的重点不是依赖 AI，而是自己推导、理解。\n",
    "2. **AI 的作用** —— 在学习过程中，可以用 AI 来帮助梳理思路，比如：要实现什么功能、实现的思路是什么、卷积的计算步骤如何展开。但不是让 AI 直接把代码写出来，而是在完全理解之后，自己动手实现。\n",
    "3. **意义** —— CNN 是一个经典的神经网络架构。面试或简历上都可以涉及。即使面试官不会要求你用 keras 复现网络，也一定会问到“卷积到底在做什么”。因此，这个练习有助于理清卷积的原理和计算过程，把理解落实到细节。"
   ],
   "id": "672cd13a4a7f8b9e"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### Zero-Padding",
   "id": "b37902b87c2db82"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T08:41:55.840565Z",
     "start_time": "2025-09-23T08:41:55.807093Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cnn_utils\n",
    "import importlib\n",
    "importlib.reload(cnn_utils)"
   ],
   "id": "680ecba733ce5c48",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'cnn_utils' from 'D:\\\\anaconda\\\\py1\\\\ml-teach-main\\\\cnn_utils.py'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T08:41:59.638904Z",
     "start_time": "2025-09-23T08:41:59.630408Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def zero_pad(X, pad):\n",
    "    \"\"\"\n",
    "    任务：\n",
    "    对数据集 X 中的所有图像进行零填充（padding）。填充应用于图像的高度和宽度\n",
    "    参数：\n",
    "\n",
    "    X —— 一个形状为 (m, n_H, n_W, n_C) 的 Python NumPy 数组，表示一批 m 张图像。\n",
    "\n",
    "    pad —— 整数，表示在图像的垂直和水平方向上填充的宽度。\n",
    "\n",
    "    返回值：\n",
    "\n",
    "    X_pad —— 填充后的图像数组，形状为 (m, n_H + 2 * pad, n_W + 2 * pad, n_C)。\n",
    "    \"\"\"\n",
    "    X_pad=np.pad(X,((0,0),(pad,pad),(pad,pad),(0,0)),mode='constant',constant_values=0)\n",
    "    return X_pad\n",
    "    # Todo: 实现"
   ],
   "id": "825bb3a6280f8ffc",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T08:42:02.433916Z",
     "start_time": "2025-09-23T08:42:01.596565Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 运行 + 测试 zero_pad\n",
    "np.random.seed(1)\n",
    "x = np.random.randn(4, 3, 3, 2)\n",
    "x_pad = zero_pad(x, 3)\n",
    "print (\"x.shape =\\n\", x.shape)\n",
    "print (\"x_pad.shape =\\n\", x_pad.shape)\n",
    "print (\"x[1,1] =\\n\", x[1, 1])\n",
    "print (\"x_pad[1,1] =\\n\", x_pad[1, 1])\n",
    "\n",
    "fig, axarr = plt.subplots(1, 2)\n",
    "axarr[0].set_title('x')\n",
    "axarr[0].imshow(x[0, :, :, 0])\n",
    "axarr[1].set_title('x_pad')\n",
    "axarr[1].imshow(x_pad[0, :, :, 0])\n",
    "cnn_utils.zero_pad_test(zero_pad)"
   ],
   "id": "9896a585b6bfe0c5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x.shape =\n",
      " (4, 3, 3, 2)\n",
      "x_pad.shape =\n",
      " (4, 9, 9, 2)\n",
      "x[1,1] =\n",
      " [[ 0.90085595 -0.68372786]\n",
      " [-0.12289023 -0.93576943]\n",
      " [-0.26788808  0.53035547]]\n",
      "x_pad[1,1] =\n",
      " [[0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]]\n",
      "x.shape =\n",
      " (4, 3, 3, 2)\n",
      "x_pad.shape =\n",
      " (4, 9, 9, 2)\n",
      "x[1,1] =\n",
      " [[ 0.90085595 -0.68372786]\n",
      " [-0.12289023 -0.93576943]\n",
      " [-0.26788808  0.53035547]]\n",
      "x_pad[1,1] =\n",
      " [[0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]\n",
      " [0. 0.]]\n",
      "[[0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
      "\u001B[92mAll tests passed!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 单步卷积",
   "id": "ce4832ab46fbf449"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T08:54:00.105602Z",
     "start_time": "2025-09-23T08:54:00.095758Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def conv_single_step(a_slice_prev, W, b):\n",
    "    \"\"\"\n",
    "    **功能说明：**\n",
    "    对前一层输出激活值中的一个切片（`a_slice_prev`）应用由参数 `W` 定义的一个卷积核（filter）。\n",
    "    切片：卷积窗口当前所对应的输入，这里只需要实现卷积的一步操作，不需要滑动卷积窗口\n",
    "\n",
    "    **参数：**\n",
    "\n",
    "    * `a_slice_prev` —— 输入数据的一个切片，形状为 `(f, f, n_C_prev)`\n",
    "    * `W` —— 卷积核的权重参数，窗口矩阵，形状为 `(f, f, n_C_prev)`\n",
    "    * `b` —— 卷积核的偏置参数，窗口矩阵，形状为 `(1, 1, 1)`\n",
    "\n",
    "    **返回值：**\n",
    "\n",
    "    * `Z` —— 一个标量值，表示将卷积核 `(W, b)` 与输入数据的一个切片 `x` 进行卷积的结果。\n",
    "    \"\"\"\n",
    "\n",
    "    # Todo: 实现\n",
    "    s=a_slice_prev * W\n",
    "    sum_s=np.sum(s)\n",
    "    Z=sum_s + b.item()\n",
    "    return Z"
   ],
   "id": "d4ae141b61940400",
   "outputs": [],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T08:54:02.531873Z",
     "start_time": "2025-09-23T08:54:02.517950Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 运行 + 测试conv_single_step\n",
    "np.random.seed(1)\n",
    "a_slice_prev = np.random.randn(4, 4, 3)\n",
    "W = np.random.randn(4, 4, 3)\n",
    "b = np.random.randn(1, 1, 1)\n",
    "\n",
    "Z = conv_single_step(a_slice_prev, W, b)\n",
    "print(\"Z =\", Z)\n",
    "cnn_utils.conv_single_step_test(conv_single_step)\n",
    "\n",
    "assert (type(Z) == np.float64), \"You must cast the output to numpy float 64\"\n",
    "assert np.isclose(Z, -6.999089450680221), \"Wrong value\""
   ],
   "id": "99d726b95229c8d5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z = -6.999089450680221\n",
      "\u001B[92mAll tests passed!\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 完整卷积",
   "id": "dbc0fcbbd8a8a9c4"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T09:20:43.649709Z",
     "start_time": "2025-09-23T09:20:43.624968Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def conv_forward(A_prev, W, b, hparameters):\n",
    "    \"\"\"\n",
    "    实现卷积函数的前向传播\n",
    "\n",
    "    参数：\n",
    "    A_prev -- 前一层的输出激活值，\n",
    "              形状为 (m, n_H_prev, n_W_prev, n_C_prev) 的 numpy 数组\n",
    "    W -- 卷积核的权重，\n",
    "         形状为 (f, f, n_C_prev, n_C) 的 numpy 数组\n",
    "    b -- 偏置，\n",
    "         形状为 (1, 1, 1, n_C) 的 numpy 数组\n",
    "    hparameters -- 包含超参数的 python 字典，键包括 \"stride\"和 \"pad\"， stride键对应的是步长，pad键对应的是填充\n",
    "\n",
    "    返回值：\n",
    "    Z -- 卷积后的输出，\n",
    "         形状为 (m, n_H, n_W, n_C) 的 numpy 数组\n",
    "    \"\"\"\n",
    "    # TODO： 实现\n",
    "    (m,n_H_prev,n_W_prev,n_C_prev) = A_prev.shape\n",
    "    (f,f,n_c_prev,n_C) = W.shape\n",
    "    stride = hparameters[\"stride\"]\n",
    "    pad=hparameters[\"pad\"]\n",
    "    n_H=int((n_H_prev+2*pad-f)/stride)+1\n",
    "    n_W=int((n_W_prev+2*pad-f)/stride)+1\n",
    "    Z=np.zeros((m,n_H,n_W,n_C))\n",
    "    A_prev_pad=np.pad(A_prev,((0,0),(pad,pad),(pad,pad),(0,0)),mode='constant',constant_values=0)\n",
    "    for i in range(m):\n",
    "        a_prev_pad = A_prev_pad[i]\n",
    "        for h in range(n_H):\n",
    "            vert_start = h * stride\n",
    "            vert_end = vert_start + f\n",
    "            for w in range(n_W):\n",
    "                horiz_start = w * stride\n",
    "                horiz_end = horiz_start + f\n",
    "                for c in range(n_C):\n",
    "                    a_slice_prev = a_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :]\n",
    "                    weights = W[:, :, :, c]\n",
    "                    bias = b[:, :, :, c]\n",
    "                    Z[i, h, w, c] = conv_single_step(a_slice_prev, weights, bias)\n",
    "    return Z\n",
    "\n"
   ],
   "id": "ab29a85edd6f5d25",
   "outputs": [],
   "execution_count": 21
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "可以参考一个代码模板，启发实现思路：\n",
    "\n",
    "```python\n",
    "def conv_forward(A_prev, W, b, hparameters):\n",
    "    # 从 A_prev 的形状中获取维度 (≈1 行)\n",
    "    # (m, n_H_prev, n_W_prev, n_C_prev) = None\n",
    "\n",
    "    # 从 W 的形状中获取维度 (≈1 行)\n",
    "    # (f, f, n_C_prev, n_C) = None\n",
    "\n",
    "    # 从 \"hparameters\" 中获取参数信息 (≈2 行)\n",
    "    # stride = None\n",
    "    # pad = None\n",
    "\n",
    "    # 使用给定的公式计算卷积输出的维度。\n",
    "    # 提示：使用 int() 来实现“向下取整”。(≈2 行)\n",
    "    # n_H = None\n",
    "    # n_W = None\n",
    "\n",
    "    # 用零初始化输出体 Z (≈1 行)\n",
    "    # Z = None\n",
    "\n",
    "    # 通过对 A_prev 进行填充创建 A_prev_pad\n",
    "    # A_prev_pad = None\n",
    "\n",
    "    # 遍历整个训练样本批次\n",
    "    # for i in range(None):\n",
    "\n",
    "        # 选择第 i 个训练样本的填充后激活值\n",
    "        # a_prev_pad = None\n",
    "\n",
    "        # 遍历输出体的垂直方向\n",
    "        # for h in range(None):\n",
    "\n",
    "            # 找到当前切片的垂直起始和结束位置 (≈2 行)\n",
    "            # vert_start = None\n",
    "            # vert_end = None\n",
    "\n",
    "            # 遍历输出体的水平方向\n",
    "            # for w in range(None):\n",
    "\n",
    "                # 找到当前切片的水平起始和结束位置 (≈2 行)\n",
    "                # horiz_start = None\n",
    "                # horiz_end = None\n",
    "\n",
    "                # 遍历输出体的通道数（= 卷积核数量）\n",
    "                # for c in range(None):\n",
    "\n",
    "                    # 使用边界来定义 a_prev_pad 的 (3D) 切片 (提示见单元格上方) (≈1 行)\n",
    "                    # a_slice_prev = None\n",
    "\n",
    "                    # 将 (3D) 切片与对应的卷积核 W 以及偏置 b 做卷积，得到一个输出神经元 (≈3 行)\n",
    "                    # weights = None\n",
    "                    # biases = None\n",
    "                    # Z[i, h, w, c] = None\n",
    "    # return Z\n",
    "```"
   ],
   "id": "50f0d4011c068cf8"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T09:20:47.706695Z",
     "start_time": "2025-09-23T09:20:47.584111Z"
    }
   },
   "cell_type": "code",
   "source": [
    "np.random.seed(1)\n",
    "A_prev = np.random.randn(2, 5, 7, 4)\n",
    "W = np.random.randn(3, 3, 4, 8)\n",
    "b = np.random.randn(1, 1, 1, 8)\n",
    "hparameters = {\"pad\" : 1,\n",
    "               \"stride\": 2}\n",
    "\n",
    "Z = conv_forward(A_prev, W, b, hparameters)\n",
    "z_mean = np.mean(Z)\n",
    "z_0_2_1 = Z[0, 2, 1]\n",
    "print(\"Z's mean =\\n\", z_mean)\n",
    "print(\"Z[0,2,1] =\\n\", z_0_2_1)\n",
    "\n",
    "cnn_utils.conv_forward_test_1(z_mean, z_0_2_1)\n",
    "cnn_utils.conv_forward_test_2(conv_forward)"
   ],
   "id": "7bcf644c55960212",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z's mean =\n",
      " 0.5511276474566768\n",
      "Z[0,2,1] =\n",
      " [-2.17796037  8.07171329 -0.5772704   3.36286738  4.48113645 -2.89198428\n",
      " 10.99288867  3.03171932]\n",
      "\u001B[92mFirst Test: All tests passed!\n",
      "\u001B[92mSecond Test: All tests passed!\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### 池化",
   "id": "1f202e934c24bd9f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T09:30:30.185689Z",
     "start_time": "2025-09-23T09:30:30.170868Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def pool_forward(A_prev, hparameters, mode = \"max\"):\n",
    "    \"\"\"\n",
    "    实现池化层的前向传播\n",
    "\n",
    "    参数：\n",
    "    A_prev -- 输入数据，形状为 (m, n_H_prev, n_W_prev, n_C_prev) 的 numpy 数组\n",
    "    hparameters -- 包含超参数的 python 字典，键包括 \"f\"（池化窗口大小）和 \"stride\"（步幅）\n",
    "    mode -- 要使用的池化方式，字符串类型，可选 \"max\" 或 \"average\"\n",
    "\n",
    "    返回值：\n",
    "    A -- 池化层的输出，形状为 (m, n_H, n_W, n_C) 的 numpy 数组\n",
    "    \"\"\"\n",
    "\n",
    "    # TODO: 实现\n",
    "    (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n",
    "    f = hparameters[\"f\"]\n",
    "    stride = hparameters[\"stride\"]\n",
    "    n_H = int(1 + (n_H_prev - f) / stride)\n",
    "    n_W = int(1 + (n_W_prev - f) / stride)\n",
    "    n_C = n_C_prev\n",
    "    A = np.zeros((m, n_H, n_W, n_C))\n",
    "    for i in range(m):\n",
    "        for h in range(n_H):\n",
    "            vert_start = h * stride\n",
    "            vert_end = vert_start + f\n",
    "            for w in range(n_W):\n",
    "                horiz_start = w * stride\n",
    "                horiz_end = horiz_start + f\n",
    "                for c in range(n_C):\n",
    "                    a_prev_slice = A_prev[i, vert_start:vert_end, horiz_start:horiz_end, c]\n",
    "                    if mode=='max':\n",
    "                        A[i,h,w,c] = np.max(a_prev_slice)\n",
    "                    elif mode=='average':\n",
    "                        A[i,h,w,c] = np.mean(a_prev_slice)\n",
    "    return A\n",
    "\n"
   ],
   "id": "16f4f6e6ae8462f4",
   "outputs": [],
   "execution_count": 24
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "可以参考一个代码模板，启发思路\n",
    "```python\n",
    "def pool_forward(A_prev, hparameters, mode = \"max\"):\n",
    "    # 从输入的形状中获取维度\n",
    "    (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n",
    "\n",
    "    # 从 \"hparameters\" 中获取超参数\n",
    "    f = hparameters[\"f\"]\n",
    "    stride = hparameters[\"stride\"]\n",
    "\n",
    "    # 定义输出的维度\n",
    "    n_H = int(1 + (n_H_prev - f) / stride)\n",
    "    n_W = int(1 + (n_W_prev - f) / stride)\n",
    "    n_C = n_C_prev\n",
    "\n",
    "    # 初始化输出矩阵 A\n",
    "    A = np.zeros((m, n_H, n_W, n_C))\n",
    "\n",
    "    # 遍历训练样本\n",
    "    # for i in range(None):\n",
    "\n",
    "        # 遍历输出的垂直轴\n",
    "        # for h in range(None):\n",
    "\n",
    "            # 找到当前切片的垂直起始位置和结束位置 (≈2 行)\n",
    "            # vert_start = None\n",
    "            # vert_end = None\n",
    "\n",
    "            # 遍历输出的水平轴\n",
    "            # for w in range(None):\n",
    "\n",
    "                # 找到当前切片的水平起始位置和结束位置 (≈2 行)\n",
    "                # horiz_start = None\n",
    "                # horiz_end = None\n",
    "\n",
    "                # 遍历输出的各个通道\n",
    "                # for c in range (None):\n",
    "\n",
    "                    # 使用边界来定义第 i 个训练样本在通道 c 上的当前切片 (≈1 行)\n",
    "                    # a_prev_slice = None\n",
    "\n",
    "                    # 对切片执行池化操作。\n",
    "                    # 使用 if 语句来区分不同的池化模式。\n",
    "                    # 使用 np.max 和 np.mean。\n",
    "                    # if mode == \"max\":\n",
    "                        # A[i, h, w, c] = None\n",
    "                    # elif mode == \"average\":\n",
    "                        # A[i, h, w, c] = None\n",
    "```"
   ],
   "id": "eb464492683fb87a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-09-23T09:30:37.239909Z",
     "start_time": "2025-09-23T09:30:37.206194Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Case 1: 步幅为1\n",
    "print(\"CASE 1:\\n\")\n",
    "np.random.seed(1)\n",
    "A_prev_case_1 = np.random.randn(2, 5, 5, 3)\n",
    "hparameters_case_1 = {\"stride\" : 1, \"f\": 3}\n",
    "\n",
    "A, cache = pool_forward(A_prev_case_1, hparameters_case_1, mode = \"max\")\n",
    "print(\"mode = max\")\n",
    "print(\"A.shape = \" + str(A.shape))\n",
    "print(\"A[1, 1] =\\n\", A[1, 1])\n",
    "A, cache = pool_forward(A_prev_case_1, hparameters_case_1, mode = \"average\")\n",
    "print(\"mode = average\")\n",
    "print(\"A.shape = \" + str(A.shape))\n",
    "print(\"A[1, 1] =\\n\", A[1, 1])\n",
    "\n",
    "cnn_utils.pool_forward_test_1(pool_forward)\n",
    "\n",
    "# Case 2: 步幅为2\n",
    "print(\"\\n\\033[0mCASE 2:\\n\")\n",
    "np.random.seed(1)\n",
    "A_prev_case_2 = np.random.randn(2, 5, 5, 3)\n",
    "hparameters_case_2 = {\"stride\" : 2, \"f\": 3}\n",
    "\n",
    "A, cache = pool_forward(A_prev_case_2, hparameters_case_2, mode = \"max\")\n",
    "print(\"mode = max\")\n",
    "print(\"A.shape = \" + str(A.shape))\n",
    "print(\"A[0] =\\n\", A[0])\n",
    "print()\n",
    "\n",
    "A, cache = pool_forward(A_prev_case_2, hparameters_case_2, mode = \"average\")\n",
    "print(\"mode = average\")\n",
    "print(\"A.shape = \" + str(A.shape))\n",
    "print(\"A[1] =\\n\", A[1])\n",
    "\n",
    "cnn_utils.pool_forward_test_2(pool_forward)"
   ],
   "id": "21f18fde6d7c8ea0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CASE 1:\n",
      "\n",
      "mode = max\n",
      "A.shape = (3, 3, 3)\n",
      "A[1, 1] =\n",
      " [1.14472371 0.90159072 1.65980218]\n",
      "mode = average\n",
      "A.shape = (3, 3, 3)\n",
      "A[1, 1] =\n",
      " [0.04737072 0.02592447 0.09203384]\n",
      "\u001B[92mAll tests passed!\n",
      "\n",
      "\u001B[0mCASE 2:\n",
      "\n",
      "mode = max\n",
      "A.shape = (2, 2, 3)\n",
      "A[0] =\n",
      " [[1.74481176 0.90159072 1.65980218]\n",
      " [1.74481176 1.6924546  1.65980218]]\n",
      "\n",
      "mode = average\n",
      "A.shape = (2, 2, 3)\n",
      "A[1] =\n",
      " [[-0.38268052  0.23257995  0.6259979 ]\n",
      " [-0.09525515  0.268511    0.46605637]]\n",
      "\u001B[92mAll tests passed!\n"
     ]
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "13dc9543f7966352"
  }
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